
import pandas as pd
pd.set_option('display.max_columns', None)


vehicle = pd.read_csv("vehicles.csv")


vehicle[:1000].to_csv("data_file.csv")


vehicle_2 = vehicle[vehicle["posting_date"].notna()]
vehicle_2["posting_date"] = vehicle_2["posting_date"].str[:-5]
vehicle_2["posting_date"] = pd.to_datetime(vehicle_2["posting_date"],format='%Y-%m-%dT%H:%M:%S')
df = pd.DataFrame(vehicle_2.resample('H', on='posting_date')["id"].count())
df = pd.DataFrame(list(df["id"]),list(df.index))
df.rename(columns={0:"Hourly_Transaction"},inplace=True)
df["hour"]=pd.to_datetime(df.index).strftime('%-H')
model1 = smf.ols('Hourly_Transaction ~ C(hour)', data=df).fit()
df['dehourfied_hourly_Transaction'] = model1.resid
df[:50]


from mizani.formatters import comma_format
from plotnine import *
from plotnine.stats import *
from mizani.breaks import date_breaks
from mizani.formatters import date_format
vehicle_2 = vehicle[vehicle["posting_date"].notna()]
vehicle_2["posting_date"] = vehicle_2["posting_date"].str[:-5]
vehicle_2["posting_date"] = pd.to_datetime(vehicle_2["posting_date"],format='%Y-%m-%dT%H:%M:%S')
df = pd.DataFrame(vehicle_2.resample('H', on='posting_date')["id"].count())
df = pd.DataFrame(list(df["id"]),list(df.index))
df.rename(columns={0:"Hourly_Transaction"},inplace=True)
df["hour"]=pd.to_datetime(df.index).strftime('%-H')
model1 = smf.ols('Hourly_Transaction ~ C(hour)', data=df).fit()
df['dehourfied_hourly_Transaction'] = model1.resid
b = (ggplot(df, aes(df.index, df["dehourfied_hourly_Transaction"]))) + geom_point(size=0.5) + \
geom_line(aes(group = 1)) \
+ theme(figure_size=(10, 8))\
+ theme(text=element_text(size=10))\
+ theme(axis_text_x=element_text(rotation=45, hjust=1))\
+ labs(x='Date', y='Dehourfied Hourly Secondhand Transaction Posting Number') \
+ ggtitle("Dehourfied Hourly Secondhand Transaction Posting Number")\
+ scale_x_datetime(breaks=date_breaks('1 day'), labels=(date_format('%Y-%m-%d %H')))
b

<ggplot: (1457514288261)>


from mizani.formatters import comma_format
from plotnine import *
from plotnine.stats import *
from mizani.breaks import date_breaks
from mizani.formatters import date_format
vehicle_2 = vehicle[vehicle["posting_date"].notna()]
vehicle_2["posting_date"] = vehicle_2["posting_date"].str[:-5]
vehicle_2["posting_date"] = pd.to_datetime(vehicle_2["posting_date"],format='%Y-%m-%dT%H:%M:%S')
df = pd.DataFrame(vehicle_2.resample('H', on='posting_date')["id"].count())
df = pd.DataFrame(list(df["id"]),list(df.index))
b = (ggplot(df, aes(df.index, df[0]))) + geom_point(size=0.5) + geom_line(aes(group = 1)) \
+ theme(figure_size=(10, 8))\
+ theme(text=element_text(size=10))\
+ theme(axis_text_x=element_text(rotation=45, hjust=1))\
+ labs(x='Date', y='Hourly Secondhand Transaction Posting Number') \
+ ggtitle("Hourly Secondhand Transaction Posting Number")\
+ scale_x_datetime(breaks=date_breaks('1 day'), labels=(date_format('%Y-%m-%d %H')))
b

<ggplot: (1457515941678)>


from mizani.formatters import comma_format
from plotnine import *
from plotnine.stats import *
from mizani.breaks import date_breaks
from mizani.formatters import date_format

df_1 = vehicle_2.groupby([vehicle_2['posting_date'].dt.date])["id"].count()
df_1 = pd.DataFrame(list(df_1),list(df_1.index))
b = (ggplot(df_1, aes(df_1.index, df_1[0]))) + geom_point(size=0.5) + geom_line(aes(group = 1)) \
+ theme(figure_size=(10, 8))\
+ theme(text=element_text(size=10))\
+ theme(axis_text_x=element_text(rotation=45, hjust=1))\
+ labs(x='Date', y='Daily Secondhand Transaction Posting Number') \
+ ggtitle("Daily Secondhand Transaction Posting Number")\
+ scale_x_datetime(breaks=date_breaks('1 day'), labels=(date_format('%Y-%m-%d')))
b

<ggplot: (1457518854919)>


import statsmodels.api as sm
import statsmodels.formula.api as smf
df_1 = vehicle_2.groupby([vehicle_2['posting_date'].dt.date])["id"].count()
df_1 = pd.DataFrame(list(df_1),list(df_1.index))
df_1["weekday"]=pd.to_datetime(df_1.index).strftime('%A')
df_1.rename(columns={0:"daily_transaction"},inplace=True)
model1 = smf.ols('daily_transaction ~ C(weekday)', data=df_1).fit()
df_1['deweekfied_daily_transaction'] = model1.resid
df_1


from mizani.formatters import comma_format
from plotnine import *
from plotnine.stats import *
from mizani.breaks import date_breaks
from mizani.formatters import date_format
import statsmodels.api as sm
import statsmodels.formula.api as smf
df_1 = vehicle_2.groupby([vehicle_2['posting_date'].dt.date])["id"].count()
df_1 = pd.DataFrame(list(df_1),list(df_1.index))
df_1["weekday"]=pd.to_datetime(df_1.index).strftime('%A')
df_1.rename(columns={0:"daily_transaction"},inplace=True)
model1 = smf.ols('daily_transaction ~ C(weekday)', data=df_1).fit()
df_1['deweekfied_daily_transaction'] = model1.resid
b = (ggplot(df_1, aes(df_1.index, df_1['deweekfied_daily_transaction']))) + geom_point(size=0.5) + geom_line(aes(group = 1)) \
+ theme(figure_size=(10, 8))\
+ theme(text=element_text(size=10))\
+ theme(axis_text_x=element_text(rotation=45, hjust=1))\
+ labs(x='Date', y='Deweekdayfied Daily Secondhand Transaction Posting Number') \
+ ggtitle("Deweekdayfied Daily Secondhand Transaction Posting Number")\
+ scale_x_datetime(breaks=date_breaks('1 day'), labels=(date_format('%Y-%m-%d')))
b

<ggplot: (1457517091291)>


vehicle_1 = vehicle[vehicle["model"].notna() & vehicle["year"].notna() & vehicle["manufacturer"].notna()\
                    & vehicle["price"].notna()\
                    & vehicle["odometer"].notna() & vehicle["price"]!=0]


vehicle_1["age"] = 2021-vehicle_1["year"].astype(int)

/tmp/ipykernel_130750/2315890347.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  vehicle_1["age"] = 2021-vehicle_1["year"].astype(int)


from collections import Counter
model = "ford"
list_1 = ["SUV","convertible","coupe","hatchback","pickup","sedan","truck"]
vehicle_2 = vehicle_1[vehicle_1["manufacturer"]==model]
vehicle_2 = vehicle_2[vehicle_2["type"].isin(list_1)]
vehicle_3 = vehicle_2[vehicle_2["model"].isin([x[0] for x in Counter(list(vehicle_2["model"])).most_common()][:10])]
vehicle_3_df = vehicle_3.pivot_table(index='model',columns='type',values='id',aggfunc=lambda x: len(x.unique())).fillna(0)
from statsmodels.graphics.mosaicplot import mosaic
t1 = mosaic(vehicle_2, ['model','type'],title="Models and Types of Cars Manufactured by "+model.capitalize(),\
            properties=props)


vehicle_2_df


import seaborn as sns
sns.histplot(vehicle_1["age"])

<AxesSubplot: xlabel='age', ylabel='Count'>


df = vehicle_1[vehicle_1["model"]=='f-150']
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
df.shape

/tmp/ipykernel_1568744/3826991033.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1568744/3826991033.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1568744/3826991033.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)

(3430, 27)


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import warnings
warnings.filterwarnings('ignore')
fig, axs = plt.subplots(4, 4, figsize=(20, 20))
model = "nissan"
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
fig.suptitle("Correlation Matrix of Cars Manufactured by "+model.capitalize(),\
             fontsize=20)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
df["log(price)"] = np.log(df["price"])
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][0:16]
for i in range(len(car_list)):
    matrix_1 = df[df["model"]==car_list[i]]\
    [["log(price)", "age", "odometer"]].corr()
    g1 = sns.heatmap(matrix_1,annot=True,ax=axs[i//4,i%4], vmin=-1, vmax=1)
    g1.set_title(car_list[i], fontsize=15)


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
fig, axs = plt.subplots(4, 4, figsize=(20, 20))
model = "ford"
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
fig.suptitle("Correlation Matrix of Cars Manufactured by "+model.capitalize(),\
             fontsize=20)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
df["log(price)"] = np.log(df["price"])
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][0:16]
for i in range(len(car_list)):
    matrix_1 = df[df["model"]==car_list[i]]\
    [["price", "age", "odometer"]].corr()
    g1 = sns.heatmap(matrix_1,annot=True,ax=axs[i//4,i%4], vmin=-1, vmax=1)
    g1.set_title(car_list[i], fontsize=15)

/tmp/ipykernel_2639724/1030447802.py:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_2639724/1030447802.py:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_2639724/1030447802.py:14: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)
/tmp/ipykernel_2639724/1030447802.py:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["log(price)"] = np.log(df["price"])


Counter(list(vehicle_1[vehicle_1["manufacturer"].notna()]["manufacturer"])).most_common()[:20]

[('ford', 25651),
 ('chevrolet', 18388),
 ('toyota', 11116),
 ('honda', 7005),
 ('nissan', 6750),
 ('ram', 6604),
 ('jeep', 6560),
 ('gmc', 5477),
 ('dodge', 4679),
 ('bmw', 4401),
 ('hyundai', 3558),
 ('mercedes-benz', 3478),
 ('subaru', 3394),
 ('kia', 2952),
 ('volkswagen', 2559),
 ('lexus', 2320),
 ('chrysler', 2134),
 ('cadillac', 2086),
 ('audi', 2019),
 ('mazda', 1602)]


[x[0] for x in Counter(list(vehicle_1[vehicle_1["manufacturer"].notna()]["manufacturer"])).most_common()][:5]

['ford', 'chevrolet', 'toyota', 'honda', 'nissan']


from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
manu_list = [x[0] for x in Counter(list(vehicle_1[vehicle_1["manufacturer"].notna()]["manufacturer"])).most_common()][:5]
n=4
fig, axs = plt.subplots(5, 2, figsize=(8*2, 8*5))
for i in range(len(manu_list)):
    model=manu_list[i]
    df = vehicle_1[vehicle_1["manufacturer"]==model]
    df["year"] = df["year"].astype(int)
    df["age"] = 2021 - df["year"]
    df["price"] = df["price"].astype(float)
    df=df[["model","manufacturer","condition","type","price","age","odometer"]]
    df = df[(df["odometer"]<=300000) & (df["odometer"]>0) & (df["age"]<40)]
    df=df[df["condition"].isin([x[0] for x in Counter(list(df["condition"])).most_common()][0:6])]
    g = sns.violinplot(data=df, x="condition", y="odometer",ylim=[-20000,3000],ax=axs[i,0])
    g.set_title("Mileage Distribution and Conditions of\n Vehicles Manufactured by "+model.capitalize(),fontsize=20)
    df=df[df["type"].isin([x[0] for x in Counter(list(df["type"])).most_common()][1:6])]
    g = sns.violinplot(data=df, x="condition", y="age",ylim=[-20000,3000],ax=axs[i,1])
    g.set_title("Age Distribution and Conditions of\n Vehicles Manufactured by "+model.capitalize(),fontsize=20)


from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
manu_list = [x[0] for x in Counter(list(vehicle_1[vehicle_1["manufacturer"].notna()]["manufacturer"])).most_common()][:5]
n=4
fig, axs = plt.subplots(5, 2, figsize=(8*2, 8*5))
for i in range(len(manu_list)):
    model=manu_list[i]
    df = vehicle_1[vehicle_1["manufacturer"]==model]
    df["year"] = df["year"].astype(int)
    df["age"] = 2021 - df["year"]
    df["price"] = df["price"].astype(float)
    df=df[["model","manufacturer","condition","type","price","age","odometer"]]
    df = df[(df["odometer"]<=300000) & (df["odometer"]>0) & (df["age"]<40)]
    df=df[df["type"].isin([x[0] for x in Counter(list(df["type"])).most_common()][0:6])]
    g = sns.violinplot(data=df, x="type", y="odometer",ylim=[-20000,3000],ax=axs[i,0])
    g.set_title("Mileage Distribution and Types of\n Vehicles Manufactured by "+model.capitalize(),fontsize=20)
    df=df[df["type"].isin([x[0] for x in Counter(list(df["type"])).most_common()][1:6])]
    g = sns.violinplot(data=df, x="type", y="age",ylim=[-20000,3000],ax=axs[i,1])
    g.set_title("Age Distribution and Types of\n Vehicles Manufactured by "+model.capitalize(),fontsize=20)


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
model="bmw"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Price and Age of Cars Manufactured by "+\
             model.capitalize()+" of Different Models", fontsize=20)
print("Price and Mileage of Cars Manufactured by "+\
      model.capitalize()+" of Different Models")
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]]
    g1 = sns.regplot(x="age",y="price",data=df_1,ax=axs[i//n,i%n],\
                     scatter_kws={'alpha':0.1},lowess=True,\
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-2,40])
    g1.set_ylim([0,80000])
    g1.set_xlabel("Age")
    g1.set_ylabel("Price")

Price and Mileage of Cars Manufactured by Bmw of Different Models

/tmp/ipykernel_2639724/8670581.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_2639724/8670581.py:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_2639724/8670581.py:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


import warnings
warnings.filterwarnings('ignore')
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
df_1 = vehicle_1[vehicle_1["model"]=="f-150"]
df_1["log_price"] = np.log(df_1["price"])
model1 = sm.RLM(df_2["log_price"], sm.add_constant(df_2["age"]), M=sm.robust.norms.HuberT(t=2)).fit()
model1.params["age"]

-0.1308455505353119


import sklearn.decomposition as skd
import sklearn.preprocessing as skp
df_1[["price","age","odometer"]] = df_1[["price","age","odometer"]].astype(float)
df_1[["price","age","odometer"]] = skp.scale(df_1[["price","age","odometer"]])
df_1["log price"] = np.log(df_1["price"])
df_1 = df_1[["model","price","log price","age","odometer"]]


pca_model = skd.PCA().fit(df_1[["age","odometer"]])


pca_model.components_

array([[ 0.70710678,  0.70710678],
       [-0.70710678,  0.70710678]])


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
import warnings
warnings.filterwarnings('ignore')
model=""
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price and Age of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=30)
print("Price and Age of Cars Manufactured by "+model.capitalize()+" of Different Models")
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
    df_1["log_price"] = np.log(df_1["price"])
    df_2 = df_1[(df_1["age"]>=2) & (df_1["age"]<=15)]
    model1 = smf.ols('log_price ~ age', data=df_2).fit()
    g1 = sns.regplot(x="age",y=np.log(df_1["price"]),data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=False,\
                     robust=True,ci=None,\
                    line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-2,30])
    g1.set_ylim([7.5,12.5])
    g1.set_xlabel("Age", fontsize=10)
    g1.set_ylabel("Logged Price", fontsize=10)
    g1.text(10,12,"y="+str(round(model1.params["age"],3))+"x+"+str(round(model1.params["Intercept"],2)), fontsize=15)
    g1.text(10,11.5,"age p-value: "+str(round(model1.pvalues["age"],3)), fontsize=15)
    try: 
        g1.text(10,11,"F p-value: "+str(round(model1.f_pvalue,3)), fontsize=15)
    except:
        pass
    g1.text(10,10.5,"R2: "+str(round(model1.rsquared,3)), fontsize=15)

Price and Age of Cars Manufactured by Ford of Different Models


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
import warnings
warnings.filterwarnings('ignore')
model="ford"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price and Age of Cars Manufactured by "+model.capitalize()+\
             " of Different Models\n Robust Regression Huber Method", fontsize=30)
print("Price and Age of Cars Manufactured by "+model.capitalize()+" of Different Models")
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
    df_1["log_price"] = np.log(df_1["price"])
    df_2 = df_1[(df_1["age"]>=1) & (df_1["age"]<=np.quantile(list(df_1["age"]), .95))]
    data = sm.datasets.stackloss.load()
    data.exog = sm.add_constant(data.exog)
    try:
        model1 = sm.RLM(df_2["log_price"], sm.add_constant(df_2["age"]), M=sm.robust.norms.HuberT(t=2)).fit()
    except:
        pass
    g1 = sns.regplot(x="age",y=np.log(df_1["price"]),data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=False,\
                     robust=True,ci=None,\
                    line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-2,30])
    g1.set_ylim([7.5,12.5])
    g1.set_xlabel("Age", fontsize=10)
    g1.set_ylabel("Logged Price", fontsize=10)
    try:
        g1.text(10,12,"y="+str(round(model1.params["age"],3))+"x+"+str(round(model1.params["const"],2)), fontsize=15)
    except:
        pass

Price and Age of Cars Manufactured by Ford of Different Models


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
import warnings
warnings.filterwarnings('ignore')

manufacturer_list = []
model_list = []
dict_age_depre_rate = {}
age_const = []
dict_mileage_depre_rate = {}
mileage_const = []
for model in [x[0] for x in Counter(list(vehicle_1[vehicle_1["manufacturer"].notna()]\
                                         ["manufacturer"])).\
              most_common()][:20]:
    n = 4
    print("Price and Age of Cars Manufactured by "+model.capitalize()+" of Different Models")
    df = vehicle_1[vehicle_1["manufacturer"]==model]
    df["year"] = df["year"].astype(int)
    df["age"] = 2021 - df["year"]
    df["price"] = df["price"].astype(float)
    car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                           ["model"])).most_common()][:n**2]
    for i in range(len(car_list)):
        df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
        df_1["log_price"] = np.log(df_1["price"])
        df_2 = df_1[(df_1["age"]>=2) & (df_1["age"]<=15) & (df_1["age"]<=np.quantile\
                                                            (list(df_1["age"]), .90))]
        df_3 = df_1[(df_1["odometer"]>=8000) & (df_1["odometer"]<=300000)\
                & (df_1["odometer"]<=np.quantile(list(df_1["odometer"]), .85))]
        try:
            model1 = sm.RLM(df_2["log_price"], sm.add_constant(df_2["age"]), \
                            M=sm.robust.norms.HuberT(t=2)).fit()
            model2 = sm.RLM(df_3["log_price"], sm.add_constant(df_3["odometer"]), \
                            M=sm.robust.norms.HuberT(t=2)).fit()
            age_const.append(round(model1.params["const"],4))
            mileage_const.append(round(model2.params["const"],4))
            dict_age_depre_rate[model+" "+car_list[i]] = round(model1.params["age"],4)
            dict_mileage_depre_rate[model+" "+car_list[i]] = round(model2.params["odometer"]\
                                                                   *10**6,3)   
            manufacturer_list.append(model)
            model_list.append(car_list[i])
        except:
            pass

Price and Age of Cars Manufactured by Ford of Different Models
Price and Age of Cars Manufactured by Chevrolet of Different Models
Price and Age of Cars Manufactured by Toyota of Different Models
Price and Age of Cars Manufactured by Honda of Different Models
Price and Age of Cars Manufactured by Nissan of Different Models
Price and Age of Cars Manufactured by Ram of Different Models
Price and Age of Cars Manufactured by Jeep of Different Models
Price and Age of Cars Manufactured by Gmc of Different Models
Price and Age of Cars Manufactured by Dodge of Different Models
Price and Age of Cars Manufactured by Bmw of Different Models
Price and Age of Cars Manufactured by Hyundai of Different Models
Price and Age of Cars Manufactured by Mercedes-benz of Different Models
Price and Age of Cars Manufactured by Subaru of Different Models
Price and Age of Cars Manufactured by Kia of Different Models
Price and Age of Cars Manufactured by Volkswagen of Different Models
Price and Age of Cars Manufactured by Lexus of Different Models
Price and Age of Cars Manufactured by Chrysler of Different Models
Price and Age of Cars Manufactured by Cadillac of Different Models
Price and Age of Cars Manufactured by Audi of Different Models
Price and Age of Cars Manufactured by Mazda of Different Models


print(len(dict_age_depre_rate.keys()),\
      len(dict_age_depre_rate.values()),len(dict_mileage_depre_rate.values()),\
      len(age_const),len(mileage_const),len(manufacturer_list),len(model_list))

310 310 310 310 310 310 310


df_4 = pd.DataFrame({"model and manufacturer":dict_age_depre_rate.keys(),\
              "age_depre_rate":dict_age_depre_rate.values(),\
              "mileage_depre_rate (*10**6)":dict_mileage_depre_rate.values(),\
              "age_const":age_const,\
              "mileage_const":mileage_const,\
              "model":model_list,\
              "manufacturer":manufacturer_list})
df_4["age_0_price"]=np.exp(df_4["age_const"])
df_4["mileage_0_price"]=np.exp(df_4["mileage_const"])
df_4.to_csv("car_depre_rate.csv")
df_4


vehicle["model and manufacturer"] = vehicle["manufacturer"]+" "+vehicle["model"]
df_5 = vehicle[vehicle["model and manufacturer"].isin(df_4["model and manufacturer"])].\
pivot_table(index='model and manufacturer',columns='type',values='id',aggfunc=lambda x: len(x.unique())).fillna(0)
df_6 = (df_5.div(df_5.sum(axis=1), axis=0)*100).round(2).astype(str) + '%'


vehicle["model and manufacturer"] = vehicle["manufacturer"]+" "+vehicle["model"]
df_5 = vehicle[vehicle["model and manufacturer"].isin(df_4["model and manufacturer"])].\
pivot_table(index='model and manufacturer',columns='type',values='id',aggfunc=lambda x: len(x.unique())).fillna(0)
df_6 = (df_5.div(df_5.sum(axis=1), axis=0)*100).round(2).astype(str) + '%'
df_6["types"] = np.empty((len(df_6), 0)).tolist()
df_6.insert(len(df_6.columns),"types_str", "")
for i in range(len(df_6.columns)-2):
    for j in range(len(df_6.index)):
        if float(df_6.iloc[j,i][:-1])>=25:
            df_6["types"][j].append(df_6.columns[i])
for j in range(len(df_6.index)):
    df_6["types_str"][j] = ", ".join(df_6["types"][j])
df_4 = df_4.merge(df_6[["types","types_str"]],right_on="model and manufacturer",left_on=df_6.index)
df_4


g = sns.regplot(x="age_depre_rate",y="mileage_depre_rate (*10**6)",\
data=df_4,scatter_kws={'alpha':0.3},lowess=False,\
                     robust=True,ci=None,\
                    line_kws={"color": "red"})
g.set_xlim([-0.2,0])
g.set_ylim([-20,0])
g.set_title("Age and Mileage Depreciation Rate for 310 Models of Cars", fontsize=15)
g.set_xlabel("Age Depreciation Rate", fontsize=10)
g.set_ylabel("Mileage Depreciation Rate", fontsize=10)

Text(0, 0.5, 'Mileage Depreciation Rate')


import numpy as np 
import pylab 
import scipy.stats as stats
g2 = stats.probplot(df_4["mileage_depre_rate (*10**6)"], dist="norm", plot=pylab)
pylab.show()


import numpy as np 
import pylab 
import scipy.stats as stats
stats.probplot(df_4["age_depre_rate"], dist="norm", plot=pylab)
pylab.show()


import numpy as np 
import pylab 
import scipy.stats as stats
stats.probplot(df_4["age_const"], dist="norm", plot=pylab)
pylab.show()


import numpy as np 
import pylab 
import scipy.stats as stats
stats.probplot(df_4["mileage_const"], dist="norm", plot=pylab)
pylab.show()


df_4['age_0_price'].sort_values(['age_0_price'])

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [60], in <cell line: 1>()
----> 1 df_4['age_0_price'].sort_values(['age_0_price'])

File ~/.local/lib/python3.10/site-packages/pandas/util/_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File ~/.local/lib/python3.10/site-packages/pandas/core/series.py:3744, in Series.sort_values(self, axis, ascending, inplace, kind, na_position, ignore_index, key)
   3742 inplace = validate_bool_kwarg(inplace, "inplace")
   3743 # Validate the axis parameter
-> 3744 self._get_axis_number(axis)
   3746 # GH 5856/5853
   3747 if inplace and self._is_cached:

File ~/.local/lib/python3.10/site-packages/pandas/core/generic.py:554, in NDFrame._get_axis_number(cls, axis)
    550 @final
    551 @classmethod
    552 def _get_axis_number(cls, axis: Axis) -> int:
    553     try:
--> 554         return cls._AXIS_TO_AXIS_NUMBER[axis]
    555     except KeyError:
    556         raise ValueError(f"No axis named {axis} for object type {cls.__name__}")

TypeError: unhashable type: 'list'


import numpy as np 
import pylab 
import scipy.stats as stats
stats.probplot(df_4[df_4['age_0_price']!=df_4['age_0_price'].max()]["age_0_price"], dist="norm", plot=pylab)
pylab.show()


set(list(vehicle["type"]))

{'SUV',
 'bus',
 'convertible',
 'coupe',
 'hatchback',
 'mini-van',
 nan,
 'offroad',
 'other',
 'pickup',
 'sedan',
 'truck',
 'van',
 'wagon'}


from collections import Counter
Counter(list(vehicle["type"])).most_common()

[(nan, 92858),
 ('sedan', 87056),
 ('SUV', 77284),
 ('pickup', 43510),
 ('truck', 35279),
 ('other', 22110),
 ('coupe', 19204),
 ('hatchback', 16598),
 ('wagon', 10751),
 ('van', 8548),
 ('convertible', 7731),
 ('mini-van', 4825),
 ('offroad', 609),
 ('bus', 517)]


from collections import Counter
n = 3
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Age and Mileage Depreciation Rate for 310 Models", fontsize=30)
type_list = [x[0] for x in Counter(list(vehicle["type"])).most_common()]
type_list = [x for x in type_list if str(x) != 'nan'][:n**2]
for i in range(len(type_list)):
    car_type = type_list[i]
    new_df = df_4[df_4["types_str"].str.contains(car_type,case=False)]
    g = sns.histplot(x="age_depre_rate",\
    data=new_df,ax=axs[i//n,i%n],kde=True)
    g.set_xlim([-0.2,0])
    g.set_title("Age Depreciation Rate\n for "+car_type.capitalize(), fontsize=10)
    g.set_xlabel("Age Depreciation Rate", fontsize=10)


from collections import Counter
n = 3
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Age and Mileage Depreciation Rate for 310 Models", fontsize=30)
type_list = [x[0] for x in Counter(list(vehicle["type"])).most_common()]
type_list = [x for x in type_list if str(x) != 'nan'][:n**2]
for i in range(len(type_list)):
    car_type = type_list[i]
    new_df = df_4[df_4["types_str"].str.contains(car_type,case=False)]
    g = sns.regplot(x="age_depre_rate",y="mileage_depre_rate (*10**6)",\
    data=new_df,scatter_kws={'alpha':0.3},lowess=False,\
                         robust=True,ci=None,\
                        line_kws={"color": "red"},ax=axs[i//n,i%n])
    g.set_xlim([-0.2,0])
    g.set_ylim([-20,0])
    g.set_title("Age and Mileage Depreciation Rate\n for "+car_type.capitalize(), fontsize=10)
    g.set_xlabel("Age Depreciation Rate", fontsize=10)
    g.set_ylabel("Mileage Depreciation Rate", fontsize=10)


g = sns.regplot(x="age_0_price",y="mileage_0_price",\
data=df_4,scatter_kws={'alpha':0.3},lowess=True,\
                     robust=False,\
                    line_kws={"color": "red"})
g.map_dataframe(sns.lineplot, 'x', 'y_line', color='g')
g.set_xlim([0,100000])
g.set_ylim([0,100000])
g.set_title(" Prices for 310 Models of Cars When Age and Mileage are 0", fontsize=15)
g.set_xlabel("Price When Age is 0", fontsize=10)
g.set_ylabel("Price When Mileage is 0", fontsize=10)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [63], in <cell line: 5>()
      1 g = sns.regplot(x="age_0_price",y="mileage_0_price",\
      2 data=df_4,scatter_kws={'alpha':0.3},lowess=True,\
      3                      robust=False,\
      4                     line_kws={"color": "red"})
----> 5 g.map_dataframe(sns.lineplot, 'x', 'y_line', color='g')
      6 g.set_xlim([0,100000])
      7 g.set_ylim([0,100000])

AttributeError: 'AxesSubplot' object has no attribute 'map_dataframe'


fig, ax = plt.subplots(figsize=(6, 4))
g = sns.regplot(x="age_0_price",y="mileage_0_price",\
data=df_4,scatter_kws={'alpha':0.3},lowess=True,\
                     robust=False,\
                    line_kws={"color": "red"}, ax=ax)
g.set_xlim([0,100000])
g.set_ylim([0,100000])
g.set_title(" Prices for 310 Models of Cars When Age and Mileage are 0", fontsize=15)
g.set_xlabel("Price When Age is 0", fontsize=10)
g.set_ylabel("Price When Mileage is 0", fontsize=10)
p2 = sns.lineplot(x=np.linspace(100000,10), y=np.linspace(100000,10), color='g', ax=ax)


g = sns.regplot(x="age_0_price",y="mileage_0_price",\
data=df_4,scatter_kws={'alpha':0.3},lowess=True,\
                     robust=False,\
                    line_kws={"color": "red"})
g.set_xlim([0,100000])
g.set_ylim([0,100000])
g.set_title(" Prices for 310 Models of Cars When Age and Mileage are 0", fontsize=15)
g.set_xlabel("Price When Age is 0", fontsize=10)
g.set_ylabel("Price When Mileage is 0", fontsize=10)

Text(0, 0.5, 'Price When Mileage is 0')


g = sns.regplot(x="age_0_price",y="mileage_0_price",\
data=df_4,scatter_kws={'alpha':0.3},lowess=False,\
                     robust=False,\
                    line_kws={"color": "red"})
g.set_xlim([0,100000])
g.set_ylim([0,100000])
g.set_title(" Prices for 310 Models of Cars When Age and Mileage are 0", fontsize=15)
g.set_xlabel("Price When Age is 0", fontsize=10)
g.set_ylabel("Price When Mileage is 0", fontsize=10)

Text(0, 0.5, 'Price When Mileage is 0')


g = sns.regplot(x="age_0_price",y="mileage_0_price",\
data=df_4,scatter_kws={'alpha':0.3},lowess=False,\
                     robust=True,\
                    line_kws={"color": "red"})
g.set_xlim([0,100000])
g.set_ylim([0,100000])
g.set_title(" Prices for 310 Models of Cars When Age and Mileage are 0", fontsize=15)
g.set_xlabel("Price When Age is 0", fontsize=10)
g.set_ylabel("Price When Mileage is 0", fontsize=10)

Text(0, 0.5, 'Price When Mileage is 0')


model="dodge"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price and Mileage of Cars Manufactured by "+\
             model.capitalize()+" of Different Models", fontsize=30)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
    df_1["log_price"] = np.log(df_1["price"])
    df_2 = df_1[(df_1["odometer"]>=8000) & (df_1["odometer"]<=300000)\
                & (df_1["odometer"]<=np.quantile(list(df_1["odometer"]), .85))]
    data = sm.datasets.stackloss.load()
    data.exog = sm.add_constant(data.exog)
    try:
        model1 = sm.RLM(df_2["log_price"], sm.add_constant(df_2["odometer"]),\
                        M=sm.robust.norms.HuberT(t=2)).fit()
    except:
        pass
    g1 = sns.regplot(x="odometer",y=np.log(df_2["price"]),data=df_2,ax=axs[i//n,i%n],\
                     scatter_kws={'alpha':0.1},lowess=False,\
                     robust=True,ci=None,\
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-5000,280000])
    g1.set_ylim([7.5,12])
    g1.set_xlabel("Mileage", fontsize=10)
    g1.set_ylabel("Logged Price", fontsize=10)
    try:
        g1.text(50000,11.5,"y="+str(round(model1.params["odometer"],9))+"x+"+\
                str(round(model1.params["const"],2)), fontsize=15)
    except:
        pass


model="bmw"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price and Mileage of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=30)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"]
.astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
    df_1["log_price"] = np.log(df_1["price"])
    df_2 = df_1[(df_1["odometer"]>=1000) & (df_1["odometer"]<=200000)]
    model1 = smf.ols('log_price ~ odometer', data=df_2).fit()
    g1 = sns.regplot(x="odometer",y=np.log(df_1["price"]),data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=False,\
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-5000,280000])
    g1.set_ylim([7.5,12])
    g1.set_xlabel("Mileage", fontsize=10)
    g1.set_ylabel("Logged Price", fontsize=10)
    g1.text(80000,11.5,"y="+str(round(model1.params["odometer"],8))+"x+"+str(round(model1.params["Intercept"],2)), fontsize=15)
    g1.text(80000,11,"mileage p-value: "+str(round(model1.pvalues["odometer"],3)), fontsize=15)
    g1.text(80000,10.5,"F p-value: "+str(round(model1.f_pvalue,3)), fontsize=15)
    g1.text(80000,10,"R2: "+str(round(model1.rsquared,3)), fontsize=15)


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
model="nissan"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price, age and Mileage of Cars Manufactured by "+\
             model.capitalize()+" of Different Models", fontsize=30)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
    df_1["log_price"] = np.log(df_1["price"])
    #pca_model = skd.PCA().fit(df_hept_norm.iloc[:,:7])
    df_2 = df_1[(df_1["odometer"]>=10) & (df_1["odometer"]<=300000)\
                & (df_1["age"]>=1) & (df_1["age"]<=20)]
    model1 = smf.ols('log_price ~ age + odometer', data=df_2).fit()
    g1 = sns.regplot(x=[0],y=[0],data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=True,\
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([0,10])
    g1.set_ylim([0,10])
    g1.set_xlabel("", fontsize=10)
    g1.set_ylabel("", fontsize=10)
    g1.text(1,8,model.capitalize()+"   "+car_list[i].capitalize(), fontsize=15)
    g1.text(1,6,"ln(price)="+str(round(model1.params["odometer"],8))+"*mileage\n"\
            +str(round(model1.params["age"],3))+"*age+"\
            +str(round(model1.params["Intercept"],2)), fontsize=15)
    g1.text(1,4,"mileage p-value: "+str(round(model1.pvalues["odometer"],3)), fontsize=15)
    g1.text(1,3,"age p-value: "+str(round(model1.pvalues["age"],3)), fontsize=15)
    g1.text(1,2,"F p-value: "+str(round(model1.f_pvalue,3)), fontsize=15)
    g1.text(1,1,"R2: "+str(round(model1.rsquared,3)), fontsize=15)


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
import sklearn.decomposition as skd
import sklearn.preprocessing as skp

model="nissan"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price, age and Mileage of Cars Manufactured by "+\
             model.capitalize()+" of Different Models\n Using PCA regression", fontsize=30)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
    df_1["log_price"] = np.log(df_1["price"])
    #pca_model = skd.PCA().fit(df_hept_norm.iloc[:,:7])
    df_2 = df_1[(df_1["odometer"]>=10) & (df_1["odometer"]<=300000)\
                & (df_1["age"]>=1) & (df_1["age"]<=20)]
    df_2[["price","age","odometer"]] = df_2[["price","age","odometer"]].astype(float)
    df_2[["price","age","odometer"]] = skp.scale(df_2[["price","age","odometer"]])
    df_2 = df_2[["model","price","log_price","age","odometer"]]
    pca_model = skd.PCA().fit(df_2[["age","odometer"]])
    df_2[["PC1","PC2"]] = pca_model.transform(df_2[["age","odometer"]])
    model1 = smf.ols('log_price ~ PC1', data=df_2).fit()
    g1 = sns.regplot(x=[0],y=[0],data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=True,\
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([0,10])
    g1.set_ylim([0,10])
    g1.set_xlabel("", fontsize=10)
    g1.set_ylabel("", fontsize=10)
    g1.text(1,9,model.capitalize()+"   "+car_list[i].capitalize(), fontsize=15)
    g1.text(1,8,"ln(price)="\
            +str(round(model1.params["PC1"],3))+"*PC1+"\
            +str(round(model1.params["Intercept"],2)), fontsize=15)
    g1.text(1,7,"PC1: ("+str(round(pca_model.components_[0][0],3))\
            +", "+str(round(pca_model.components_[0][1],3))+")", fontsize=15)
    g1.text(1,6,"PC1 p-value: "+str(round(model1.pvalues["PC1"],3)), fontsize=15)
    g1.text(1,5,"F p-value: "+str(round(model1.f_pvalue,3)), fontsize=15)
    g1.text(1,4,"R2: "+str(round(model1.rsquared,3)), fontsize=15)
    g1.text(1,2,"PC1,PC2 variance: \n"+str(round(pca_model.explained_variance_[0],3))\
            +", "+str(round(pca_model.explained_variance_[1],3)), fontsize=15)


model="ford"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price and Mileage of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=30)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]] 
    df_1["log_price"] = np.log(df_1["price"])
    df_2 = df_1[(df_1["odometer"]>=1000) & (df_1["odometer"]<=200000)]
    model1 = smf.ols('odometer ~ age', data=df_2).fit()
    g1 = sns.regplot(x="odometer",y=np.log(df_1["price"]),data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=True,\
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-5000,280000])
    g1.set_ylim([7.5,12])
    g1.set_xlabel("Age", fontsize=10)
    g1.set_ylabel("Mileage", fontsize=10)
    g1.text(80000,11.5,"y="+str(round(model1.params["odometer"],8))+"x+"+str(round(model1.params["Intercept"],2)), fontsize=15)
    g1.text(80000,11,"Age p-value: "+str(round(model1.pvalues["odometer"],3)), fontsize=15)


model="chevrolet"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Age and Mileage of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=20)
print("Price and Logged Age of Cars Manufactured by "+model.capitalize()+" of Different Models")
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]]
    df_2 = df_1[(df_1["odometer"]>=10) & (df_1["odometer"]<=300000)\
                & (df_1["age"]>=1) & (df_1["age"]<=15)]
    model1 = smf.ols('odometer ~ age', data=df_2).fit()
    g1 = sns.regplot(x="age",y="odometer",data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1}\
                     ,lowess=True, \
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-2,40])
    g1.set_ylim([-2000,340000])
    g1.set_xlabel("Age")
    g1.set_ylabel("Mileage")
    g1.text(5,280000,"odometer="+str(round(model1.params["age"],3))+"*age\n+"\
            +str(round(model1.params["Intercept"],2)), fontsize=15)
    g1.text(5,260000,"age p-value: "+str(round(model1.pvalues["age"],3)), fontsize=15)
    g1.text(5,240000,"F p-value: "+str(round(model1.f_pvalue,3)), fontsize=15)
    g1.text(5,220000,"R2: "+str(round(model1.rsquared,3)), fontsize=15)

Price and Logged Age of Cars Manufactured by Chevrolet of Different Models


import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
import sklearn.decomposition as skd
import sklearn.preprocessing as skp

model="ford"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Age and Mileage of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=20)
print("Price and Logged Age of Cars Manufactured by "+model.capitalize()+" of Different Models")
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]]
    df_2 = df_1[(df_1["odometer"]>=10) & (df_1["odometer"]<=300000)\
                & (df_1["age"]>=1) & (df_1["age"]<=15)]
    df_2[["price","age","odometer"]] = df_2[["price","age","odometer"]].astype(float)
    df_2[["price","age","odometer"]] = skp.scale(df_2[["price","age","odometer"]])
    df_2["log_price"] = np.log(df_2["price"])
    df_2 = df_2[["model","price","log_price","age","odometer"]]
    pca_model = skd.PCA().fit(df_2[["age","odometer"]])
    df_2[["PC1","PC2"]] = pca_model.transform(df_2[["age","odometer"]])
    model1 = smf.ols('odometer ~ age', data=df_2).fit()
    g1 = sns.regplot(x="age",y="odometer",data=df_2,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1}\
                     ,lowess=True, \
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-2,4])
    g1.set_ylim([-2,4])
    g1.set_xlabel("Nomalized Age")
    g1.set_ylabel("Nomalized Mileage")
    # Scatterplot of disaggregated data

    # Add variable unit vector projections
    X = pca_model.transform(df_2[["age","odometer"]])
    V = pca_model.transform(np.identity(X.shape[1]))
    for i, v in enumerate(V):
        g1.annotate(df_2[["PC1","PC2"]].columns[i], 
                     xy=(0,0), xytext=v[:2]*3, 
                     fontsize=15, color='orange',
                     arrowprops=dict(
                        arrowstyle='<-', linewidth=2, color='orange'))
    g1.text(-2,3.5,"odometer="+str(round(model1.params["age"],3))+"*age+"\
            +str(round(model1.params["Intercept"],2)), fontsize=15)
    g1.text(-2,3,"age p-value: "+str(round(model1.pvalues["age"],3)), fontsize=15)
    g1.text(2,3,"F p-value: "+str(round(model1.f_pvalue,3)), fontsize=15)
    g1.text(-2,2.5,"R2: "+str(round(model1.rsquared,3)), fontsize=15)

Price and Logged Age of Cars Manufactured by Ford of Different Models


model="nissan"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Age and Logged Mileage of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=20)
print("Price and Logged Age of Cars Manufactured by "+model.capitalize()+" of Different Models")
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]]
    g1 = sns.regplot(x=np.log(df_1["age"]),y=np.log(df_1["odometer"]),data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1}\
                     ,lowess=True, \
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-0.2,4])
    g1.set_ylim([9,13])
    g1.set_xlabel("Logged Age")
    g1.set_ylabel("Logged Mileage")

Price and Logged Age of Cars Manufactured by Nissan of Different Models

/tmp/ipykernel_1568744/1396034511.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1568744/1396034511.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1568744/1396034511.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


Counter(list(vehicle_1[vehicle_1["manufacturer"].notna()]["manufacturer"])).most_common()[:20]

[('ford', 25651),
 ('chevrolet', 18388),
 ('toyota', 11116),
 ('honda', 7005),
 ('nissan', 6750),
 ('ram', 6604),
 ('jeep', 6560),
 ('gmc', 5477),
 ('dodge', 4679),
 ('bmw', 4401),
 ('hyundai', 3558),
 ('mercedes-benz', 3478),
 ('subaru', 3394),
 ('kia', 2952),
 ('volkswagen', 2559),
 ('lexus', 2320),
 ('chrysler', 2134),
 ('cadillac', 2086),
 ('audi', 2019),
 ('mazda', 1602)]


model="bmw"
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Price and Mileage of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=20)
print("Price and Logged Age of Cars Manufactured by "+model.capitalize()+" of Different Models")
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]]
    g1 = sns.regplot(x="odometer",y="price",data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=True, \
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-2000,320000])
    g1.set_ylim([0,80000])
    g1.set_xlabel("Mileage")
    g1.set_ylabel("Price")

Price and Logged Age of Cars Manufactured by Bmw of Different Models

/tmp/ipykernel_2639724/3918321930.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_2639724/3918321930.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_2639724/3918321930.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Logged Price and Mileage of Cars Manufactured by "+model.capitalize()+" of Different Models", fontsize=20)
df = vehicle_1[vehicle_1["manufacturer"]==model]
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)
car_list = [x[0] for x in Counter(list(df[df["model"].notna()]\
                                       ["model"])).most_common()][:n**2]
for i in range(len(car_list)):
    df_1 = vehicle_1[vehicle_1["model"]==car_list[i]]
    g1 = sns.regplot(x="odometer",y=np.log(df_1["price"]),data=df_1,ax=axs[i//n,i%n],scatter_kws={'alpha':0.1},lowess=True, \
                     line_kws={"color": "red"})
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-2000,320000])
    g1.set_ylim([7.5,12.5])
    g1.set_xlabel("Mileage")
    g1.set_ylabel("Logged Price")

/tmp/ipykernel_2639724/3083148546.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_2639724/3083148546.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_2639724/3083148546.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Price and Age of Cars Manufactured by Ford of Different Models", fontsize=20)
for i in range(len(car_list))[:n**2]:
    df_1 = vehicle_1[vehicle_1["manufacturer"]==car_list[i]]
    g1 = sns.scatterplot(x="age",y="price",data=df_1,ax=axs[i//n,i%n],alpha=0.01)
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([0,40])
    g1.set_ylim([0,80000])
    g1.set_xlabel("Age")
    g1.set_ylabel("Price")


n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Price and Mileage of Cars of Different Manufacturers", fontsize=20)
for i in range(len(car_list))[:n**2]:
    df_1 = vehicle_1[vehicle_1["manufacturer"]==car_list[i]]
    g1 = sns.scatterplot(x="odometer",y="price",data=df_1,ax=axs[i//n,i%n],alpha=0.01)
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([0,320000])
    g1.set_ylim([0,80000])
    g1.set_xlabel("Mileage")
    g1.set_ylabel("Price")


n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Age and Mileage of Cars of Different Manufacturers", fontsize=20)
for i in range(len(car_list))[:n**2]:
    df_1 = vehicle_1[vehicle_1["manufacturer"]==car_list[i]]
    g1 = sns.scatterplot(x="age",y="odometer",data=df_1,ax=axs[i//n,i%n],alpha=0.01)
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([0,40])
    g1.set_ylim([0,320000])
    g1.set_xlabel("Age")
    g1.set_ylabel("Mileage")


n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Price and Logged Mileage of Cars of Different Manufacturers", fontsize=20)
for i in range(len(car_list))[:n**2]:
    df_1 = vehicle_1[vehicle_1["manufacturer"]==car_list[i]]
    g1 = sns.scatterplot(x=np.log(df_1["odometer"]),y="price",data=df_1,ax=axs[i//n,i%n],alpha=0.01)
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([9,13])
    g1.set_ylim([0,80000])
    g1.set_xlabel("Logged Mileage")
    g1.set_ylabel("Price")

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
n = 4
fig, axs = plt.subplots(n, n, figsize=(20, 20))
fig.suptitle("Price and Logged Age of Cars of Different Manufacturers", fontsize=20)
for i in range(len(car_list))[:n**2]:
    df_1 = vehicle_1[vehicle_1["manufacturer"]==car_list[i]]
    g1 = sns.scatterplot(x=np.log(df_1["age"]),y="price",data=df_1,ax=axs[i//n,i%n],alpha=0.01)
    g1.set_title(car_list[i], fontsize=15)
    g1.set_xlim([-0.5,4])
    g1.set_ylim([0,80000])
    g1.set_xlabel("Logged Age")
    g1.set_ylabel("Price")

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


matrix_8 = vehicle_1[["price", "age", "odometer"]].corr()
g8 = sns.heatmap(matrix_8,annot=True, vmin=-1, vmax=1)
g8.set_title('Correlation Matrix of Cars', fontsize=15)

Text(0.5, 1.0, 'Correlation Matrix of Cars')


sns.set(rc={'figure.figsize':(15,10)})
ax = sns.violinplot(data=df, x="condition", y="odometer")
ax.set_ylim([-50000, 400000]) 
ax.set_title("Condition types and Mileage of Ford f-150",fontsize=20) 
ax.set_ylabel("Mileage",fontsize=15)
ax.set_xlabel("Conditions",fontsize=15)

Text(0.5, 0, 'Conditions')


sns.set(rc={'figure.figsize':(15,10)})
ax = sns.violinplot(data=df, x="condition", y="age")
ax.set_ylim([-5, 60]) 
ax.set_title("Condition types and Age of Ford f-150",fontsize=20) 
ax.set_ylabel("Age",fontsize=15)
ax.set_xlabel("Conditions",fontsize=15)

Text(0.5, 0, 'Conditions')


sns.set(rc={'figure.figsize':(15,10)})
ax = sns.violinplot(data=df, x="condition", y="price")
ax.set_ylim([-10000, 80000]) 
ax.set_title("Condition types and Price of Ford f-150",fontsize=20) 
ax.set_ylabel("Price",fontsize=15)
ax.set_xlabel("Conditions",fontsize=15)

Text(0.5, 0, 'Conditions')


from collections import Counter
Counter(df["condition"])

Counter({'good': 949,
         'excellent': 1773,
         'like new': 237,
         'fair': 134,
         'salvage': 11,
         'new': 8})


Counter(df["fuel"])

Counter({'gas': 3097, 'diesel': 3, 'other': 11, 'hybrid': 1})


Counter(df["cylinders"])

Counter({'8 cylinders': 1372,
         '6 cylinders': 926,
         nan: 791,
         '3 cylinders': 1,
         '10 cylinders': 11,
         '4 cylinders': 8,
         'other': 3})


Counter(df["type"])

Counter({'truck': 1527,
         'pickup': 1169,
         nan: 378,
         'van': 7,
         'other': 16,
         'sedan': 6,
         'offroad': 3,
         'hatchback': 1,
         'coupe': 1,
         'SUV': 4})


import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(15,5))
fig, ax = plt.subplots()
sns.histplot(df["age"],kde=True,ax=ax)

<AxesSubplot: xlabel='age', ylabel='Count'>

<Figure size 1080x360 with 0 Axes>


import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(15,5))
fig, ax = plt.subplots()
sns.histplot(df["age"],kde=True,ax=ax)
ax.set_xlim(-5,50)

(-5.0, 50.0)

<Figure size 1080x360 with 0 Axes>


import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(20,5))
fig, ax = plt.subplots()
sns.histplot(df["odometer"],kde=True, ax=ax)
ax.set_xlim(-5000, 500000)

(-5000.0, 500000.0)

<Figure size 1440x360 with 0 Axes>


import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(20,5))
fig, ax = plt.subplots()
sns.histplot(df["odometer"],kde=True, ax=ax,binwidth=2000)
ax.set_xlim(-5000, 300000)

(-5000.0, 300000.0)

<Figure size 1440x360 with 0 Axes>


import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
logged_odometer = np.log(df["odometer"])
plt.figure(figsize=(20,5))
fig, ax = plt.subplots()
sns.histplot(logged_odometer,kde=True, ax=ax)
ax.set_xlim(0, 20)

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)

(0.0, 20.0)

<Figure size 1440x360 with 0 Axes>


import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(20,5))
sns.histplot(df["odometer"],kde=True)

<AxesSubplot: xlabel='odometer', ylabel='Count'>


sns.jointplot(x='age',y='price',data=df,kind="hist",height=10,xlim=(0,40))

<seaborn.axisgrid.JointGrid at 0x14e9a11fba00>


sns.kdeplot(x='age',y='price',data=df,cmap="Reds", shade=True, bw_adjust=.5)

/tmp/ipykernel_2985797/4078052607.py:1: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(x='age',y='price',data=df,cmap="Reds", shade=True, bw_adjust=.5)

<AxesSubplot: xlabel='age', ylabel='price'>


sns.kdeplot(x='age',y='price',data=df,cmap="Reds", shade=True, thresh=0)

/tmp/ipykernel_2985797/2079726061.py:1: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(x='age',y='price',data=df,cmap="Reds", shade=True, thresh=0)

<AxesSubplot: xlabel='age', ylabel='price'>


import matplotlib.pyplot as plt
plt.hist2d(x='age',y='price',data=df, bins=(100, 100), cmap=plt.cm.jet)
plt.show()


df = vehicle_1[vehicle_1["model"]=='f-150']
df["year"] = df["year"].astype(int)
df["age"] = (2021 - df["year"]).astype(float)
df["price"] = df["price"].astype(float)
df = df[df["price"]!=0]
df.shape

/tmp/ipykernel_4151917/4065695957.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_4151917/4065695957.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = (2021 - df["year"]).astype(float)
/tmp/ipykernel_4151917/4065695957.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)

(3430, 27)


import matplotlib.pyplot as plt
plt.figure(figsize=(15,10))
plt.scatter(x="age", y="price",data=df,alpha=0.1)
plt.xlabel("Vehicle Age (Year)", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Age of Secondhand Ford f-150", fontsize=20)
plt.xlim([-2,50])
plt.show()


import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(15,10))
plt.scatter(x=np.log(df["age"]), y="price",data=df,alpha=0.1)
plt.xlabel("Logged Vehicle Age (log(Year))", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Logged Age of Secondhand Ford f-150", fontsize=20)
plt.xlim([-0.2,4])
plt.show()


df["price"].dtypes

dtype('float64')


import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
plt.figure(figsize=(15,10))
g = sns.lmplot(x=np.log(df["age"]), y="price",data=df,alpha=0.1)
plt.xlabel("Logged Vehicle Age (log(Year))", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Logged Age of Secondhand Ford f-150", fontsize=20)
plt.xlim([-0.2,4])
plt.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [18], in <cell line: 5>()
      3 import seaborn as sns
      4 plt.figure(figsize=(15,10))
----> 5 g = sns.lmplot(x=np.log(df["age"]), y="price",data=df,alpha=0.1)
      6 plt.xlabel("Logged Vehicle Age (log(Year))", fontsize=15)
      7 plt.ylabel("Vehicle Price (USD)", fontsize=15)

TypeError: lmplot() got an unexpected keyword argument 'alpha'

<Figure size 1080x720 with 0 Axes>


import matplotlib.pyplot as plt
plt.figure(figsize=(15,10))
plt.scatter(x="odometer", y="price",data=df,alpha=0.1)
plt.xlabel("Vehicle Mileage (Mile)", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Mileage of Secondhand Ford f-150", fontsize=20)
plt.xlim([-5000,320000])
plt.show()


import matplotlib.pyplot as plt
plt.figure(figsize=(15,10))
plt.scatter(x="age", y="odometer",data=df,alpha=0.1)
plt.xlabel("Vehicle Age (Year)", fontsize=15)
plt.ylabel("Vehicle Mileage (Mile)", fontsize=15)
plt.title("Age and Mileage of Secondhand Ford f-150", fontsize=20)
plt.xlim([-2,40])
plt.ylim([-5000,350000])
plt.show()


import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(15,10))
plt.scatter(x=np.log(df["odometer"]), y="price",data=df,alpha=0.1)
plt.xlabel("Logged Vehicle Mileage (log(Mile))", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Logged Mileage of Secondhand Ford f-150", fontsize=20)
plt.xlim([9,13])
plt.show()


import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
plt.figure(figsize=(15,10))
plt.scatter(x="age", y="price",data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="new"],\
            color=colors["new"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="salvage"],\
            color=colors["salvage"],alpha=0.1)
plt.xlabel("Vehicle Age (Year)", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Age of Secondhand Ford f-150 Categorized by Condition",\
          fontsize=20)
plt.legend(['excellent: red', 'fair: orange', \
          'good: yellow', 'like new: blue', \
          'new: green', 'salvage: purple'], loc ="upper right", fontsize=15)
plt.xlim([-2,50])
plt.show()


import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
plt.figure(figsize=(15,10))
plt.scatter(x="age", y="odometer",data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.1)
plt.scatter(x="age", y="odometer",data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.1)
plt.scatter(x="age", y="odometer",data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.1)
plt.scatter(x="age", y="odometer",data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.1)
plt.scatter(x="age", y="odometer",data=df[df["condition"]=="new"],\
            color=colors["new"],alpha=0.1)
plt.scatter(x="age", y="odometer",data=df[df["condition"]=="salvage"],\
            color=colors["salvage"],alpha=0.1)
plt.xlabel("Vehicle Age (Year)", fontsize=15)
plt.ylabel("Vehicle Mileage (Mile)", fontsize=15)
plt.title("Age and Mileage of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
plt.xlim([-2,40])
plt.ylim([-5000,350000])
plt.show()


import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
plt.figure(figsize=(15,10))
plt.scatter(x="odometer", y="price",data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.1)
plt.scatter(x="odometer", y="price",data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.1)
plt.scatter(x="odometer", y="price",data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.1)
plt.scatter(x="odometer", y="price",data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.1)
plt.scatter(x="odometer", y="price",data=df[df["condition"]=="new"],\
            color=colors["new"],alpha=0.1)
plt.scatter(x="odometer", y="price",data=df[df["condition"]=="salvage"],\
            color=colors["salvage"],alpha=0.1)
plt.xlabel("Vehicle Mileage (Mile)", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Mileage of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
plt.legend(['excellent: red', 'fair: orange', \
          'good: yellow', 'like new: blue', \
          'new: green', 'salvage: purple'], loc ="upper right", fontsize=15)
plt.xlim([-5000,320000])
plt.show()


import matplotlib.pyplot as plt
import numpy as np
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
plt.figure(figsize=(15,10))
plt.scatter(x=np.log(df[df["condition"]=="excellent"]["odometer"]), y="price",\
            data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="fair"]["odometer"]), y="price",\
            data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="good"]["odometer"]), y="price",\
            data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="like new"]["odometer"]), y="price",\
            data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="new"]["odometer"]), y="price",\
            data=df[df["condition"]=="new"],\
            color=colors["new"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="salvage"]["odometer"]), y="price",\
            data=df[df["condition"]=="salvage"],\
            color=colors["salvage"],alpha=0.1)
plt.xlabel("Vehicle Logged Mileage (log(Mile))", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Logged Mileage of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
plt.legend(['excellent: red', 'fair: orange', \
          'good: yellow', 'like new: blue', \
          'new: green', 'salvage: purple'], loc ="upper right", fontsize=15)
plt.xlim([9,13])
plt.show()


import matplotlib.pyplot as plt
import numpy as np
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
plt.figure(figsize=(15,10))
plt.scatter(x=np.log(df[df["condition"]=="excellent"]["age"]), y="price",\
            data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="fair"]["age"]), y="price",\
            data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="good"]["age"]), y="price",\
            data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="like new"]["age"]), y="price",\
            data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="new"]["age"]), y="price",\
            data=df[df["condition"]=="new"],\
            color=colors["new"],alpha=0.1)
plt.scatter(x=np.log(df[df["condition"]=="salvage"]["age"]), y="price",\
            data=df[df["condition"]=="salvage"],\
            color=colors["salvage"],alpha=0.1)
plt.xlabel("Logged Vehicle Age (log(Year))", fontsize=15)
plt.ylabel("Vehicle Price (USD)", fontsize=15)
plt.title("Price and Logged Age of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
plt.legend(['excellent: red', 'fair: orange', \
          'good: yellow', 'like new: blue', \
          'new: green', 'salvage: purple'], loc ="upper right", fontsize=15)
plt.xlim([-0.2,4])
plt.show()


import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
plt.figure(figsize=(15,10))
plt.scatter(x="age", y="price",data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="new"],\
            color=colors["new"],alpha=0.1)
plt.scatter(x="age", y="price",data=df[df["condition"]=="salvage"],\
            color=colors["salvage"],alpha=0.1)
plt.xlabel("Vehicle Age", fontsize=15)
plt.ylabel("Vehicle Price", fontsize=15)
plt.title("Price and Age of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
plt.legend(['excellent: red', 'fair: orange', \
          'good: yellow', 'like new: blue', \
          'new: green', 'salvage: purple'], loc ="upper right", fontsize=15)
plt.xlim([-5,50])
plt.ylim([-2000,72000])
plt.show()


import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2, figsize=(20, 20))
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
fig.suptitle("Price and Age of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
axs[0, 0].scatter(x="age", y="price",data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.2)
axs[0, 0].set_title('excellent condition', fontsize=15)
axs[0, 0].set_xlabel("Vehicle Age (USD)", fontsize=10)
axs[0, 0].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[0, 0].set_xlim([-2,40])
axs[0, 0].set_ylim([-2000,70000])
axs[0, 0].legend(["excellent condition"],loc="upper right", fontsize=10)
axs[0, 1].scatter(x="age", y="price",data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.2)
axs[0, 1].set_title('fair condition', fontsize=15)
axs[0, 1].set_xlabel("Vehicle Age (USD)", fontsize=10)
axs[0, 1].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[0, 1].set_xlim([-2,40])
axs[0, 1].set_ylim([-2000,70000])
axs[0, 1].legend(["fair condition"],loc="upper right", fontsize=10)
axs[1, 0].scatter(x="age", y="price",data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.2)
axs[1, 0].set_title('good condition', fontsize=15)
axs[1, 0].set_xlabel("Vehicle Age (USD)", fontsize=10)
axs[1, 0].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[1, 0].set_xlim([-2,40])
axs[1, 0].set_ylim([-2000,70000])
axs[1, 0].legend(["good condition"],loc="upper right", fontsize=10)
axs[1, 1].scatter(x="age", y="price",data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.2)
axs[1, 1].set_title('like new condition', fontsize=15)
axs[1, 1].set_xlabel("Vehicle Age (USD)", fontsize=10)
axs[1, 1].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[1, 1].set_xlim([-2,40])
axs[1, 1].set_ylim([-2000,70000])
axs[1, 1].legend(["like new condition"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8cac209d0>


plt.xlim([-2,40])
plt.ylim([-5000,350000])


import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2, figsize=(20, 20))
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
fig.suptitle("Age and Mileage of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
axs[0, 0].scatter(x="age", y="odometer",data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.2)
axs[0, 0].set_title('excellent condition', fontsize=15)
axs[0, 0].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[0, 0].set_ylabel("Vehicle Mileage (Mile)", fontsize=10)
axs[0, 0].set_xlim([-2,40])
axs[0, 0].set_ylim([-5000,350000])
axs[0, 0].legend(["excellent condition"],loc="upper right", fontsize=10)
axs[0, 1].scatter(x="age", y="odometer",data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.2)
axs[0, 1].set_title('fair condition', fontsize=15)
axs[0, 1].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[0, 1].set_ylabel("Vehicle Mileage (Mile)", fontsize=10)
axs[0, 1].set_xlim([-2,40])
axs[0, 1].set_ylim([-5000,350000])
axs[0, 1].legend(["fair condition"],loc="upper right", fontsize=10)
axs[1, 0].scatter(x="age", y="odometer",data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.2)
axs[1, 0].set_title('good condition', fontsize=15)
axs[1, 0].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[1, 0].set_ylabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1, 0].set_xlim([-2,40])
axs[1, 0].set_ylim([-5000,350000])
axs[1, 0].legend(["good condition"],loc="upper right", fontsize=10)
axs[1, 1].scatter(x="age", y="odometer",data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.2)
axs[1, 1].set_title('like new condition', fontsize=15)
axs[1, 1].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[1, 1].set_ylabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1, 1].set_xlim([-2,40])
axs[1, 1].set_ylim([-5000,350000])
axs[1, 1].legend(["like new condition"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8caa1d420>


import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2, figsize=(20, 20))
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
fig.suptitle("Price and Mileage of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
axs[0, 0].scatter(x="odometer", y="price",data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.2)
axs[0, 0].set_title('excellent condition', fontsize=15)
axs[0, 0].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[0, 0].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[0, 0].set_xlim([-2000,320000])
axs[0, 0].set_ylim([-2000,70000])
axs[0, 0].legend(["excellent condition"],loc="upper right", fontsize=10)
axs[0, 1].scatter(x="odometer", y="price",data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.2)
axs[0, 1].set_title('fair condition', fontsize=15)
axs[0, 1].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[0, 1].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[0, 1].set_xlim([-2000,320000])
axs[0, 1].set_ylim([-2000,70000])
axs[0, 1].legend(["fair condition"],loc="upper right", fontsize=10)
axs[1, 0].scatter(x="odometer", y="price",data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.2)
axs[1, 0].set_title('good condition', fontsize=15)
axs[1, 0].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1, 0].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[1, 0].set_xlim([-2000,320000])
axs[1, 0].set_ylim([-2000,70000])
axs[1, 0].legend(["good condition"],loc="upper right", fontsize=10)
axs[1, 1].scatter(x="odometer", y="price",data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.2)
axs[1, 1].set_title('like new condition', fontsize=15)
axs[1, 1].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1, 1].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[1, 1].set_xlim([-2000,320000])
axs[1, 1].set_ylim([-2000,70000])
axs[1, 1].legend(["like new condition"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8ca819390>


import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(2, 2, figsize=(20, 20))
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
fig.suptitle("Price and Logged Mileage of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
axs[0, 0].scatter(x=np.log(df[df["condition"]=="excellent"]["odometer"]), y="price",\
                  data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.2)
axs[0, 0].set_title('excellent condition', fontsize=15)
axs[0, 0].set_xlabel("Logged Vehicle Mileage (log(Mile))", fontsize=10)
axs[0, 0].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[0, 0].set_xlim([9,13])
axs[0, 0].set_ylim([-2000,70000])
axs[0, 0].legend(["excellent condition"],loc="upper right", fontsize=10)
axs[0, 1].scatter(x=np.log(df[df["condition"]=="fair"]["odometer"]), y="price",\
                  data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.2)
axs[0, 1].set_title('fair condition', fontsize=15)
axs[0, 1].set_xlabel("Logged Vehicle Mileage (log(Mile))", fontsize=10)
axs[0, 1].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[0, 1].set_xlim([9,13])
axs[0, 1].set_ylim([-2000,70000])
axs[0, 1].legend(["fair condition"],loc="upper right", fontsize=10)
axs[1, 0].scatter(x=np.log(df[df["condition"]=="good"]["odometer"]), y="price",\
                  data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.2)
axs[1, 0].set_title('good condition', fontsize=15)
axs[1, 0].set_xlabel("Logged Vehicle Mileage (log(Mile))", fontsize=10)
axs[1, 0].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[1, 0].set_xlim([9,13])
axs[1, 0].set_ylim([-2000,70000])
axs[1, 0].legend(["good condition"],loc="upper right", fontsize=10)
axs[1, 1].scatter(x=np.log(df[df["condition"]=="like new"]["odometer"]), y="price",\
                  data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.2)
axs[1, 1].set_title('like new condition', fontsize=15)
axs[1, 1].set_xlabel("Logged Vehicle Mileage (log(Mile))", fontsize=10)
axs[1, 1].set_ylabel("Vehicle Price (Year)", fontsize=10)
axs[1, 1].set_xlim([9,13])
axs[1, 1].set_ylim([-2000,70000])
axs[1, 1].legend(["like new condition"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8cd5ad600>


import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(2, 2, figsize=(20, 20))
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
fig.suptitle("Price and Logged Age of Secondhand Ford f-150 Categorized by Condition", fontsize=20)
axs[0, 0].scatter(x=np.log(df[df["condition"]=="excellent"]["age"]), y="price",\
                  data=df[df["condition"]=="excellent"],\
            color=colors["excellent"],alpha=0.2)
axs[0, 0].set_title('excellent condition', fontsize=15)
axs[0, 0].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[0, 0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0, 0].set_xlim([-0.5,4])
axs[0, 0].set_ylim([-2000,72000])
axs[0, 0].legend(["excellent condition"],loc="upper right", fontsize=10)
axs[0, 1].scatter(x=np.log(df[df["condition"]=="fair"]["age"]), y="price",\
                  data=df[df["condition"]=="fair"],\
            color=colors["fair"],alpha=0.2)
axs[0, 1].set_title('fair condition', fontsize=15)
axs[0, 1].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[0, 1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0, 1].set_xlim([-0.5,4])
axs[0, 1].set_ylim([-2000,72000])
axs[0, 1].legend(["fair condition"],loc="upper right", fontsize=10)
axs[1, 0].scatter(x=np.log(df[df["condition"]=="good"]["age"]), y="price",\
                  data=df[df["condition"]=="good"],\
            color=colors["good"],alpha=0.2)
axs[1, 0].set_title('good condition', fontsize=15)
axs[1, 0].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[1, 0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1, 0].set_xlim([-0.5,4])
axs[1, 0].set_ylim([-2000,72000])
axs[1, 0].legend(["good condition"],loc="upper right", fontsize=10)
axs[1, 1].scatter(x=np.log(df[df["condition"]=="like new"]["age"]), y="price",\
                  data=df[df["condition"]=="like new"],\
            color=colors["like new"],alpha=0.2)
axs[1, 1].set_title('like new condition', fontsize=15)
axs[1, 1].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[1, 1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1, 1].set_xlim([-0.5,4])
axs[1, 1].set_ylim([-2000,72000])
axs[1, 1].legend(["like new condition"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8dc477880>


df.columns

Index(['id', 'url', 'region', 'region_url', 'price', 'year', 'manufacturer',
       'model', 'condition', 'cylinders', 'fuel', 'odometer', 'title_status',
       'transmission', 'VIN', 'drive', 'size', 'type', 'paint_color',
       'image_url', 'description', 'county', 'state', 'lat', 'long',
       'posting_date', 'posting_date_time', 'age'],
      dtype='object')


Counter(list(df["title_status"]))

Counter({'clean': 2440,
         'missing': 2,
         'lien': 33,
         'salvage': 22,
         'rebuilt': 34,
         'parts only': 2})


Counter(list(df["type"]))

Counter({'truck': 1404,
         'pickup': 1097,
         'van': 5,
         'other': 13,
         'sedan': 5,
         'offroad': 3,
         'hatchback': 1,
         'coupe': 1,
         'SUV': 4})


import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'truck':'red', 'pickup':'orange'}
fig.suptitle("Price and Age of Secondhand Ford f-150 Categorized by Type", fontsize=20)
axs[0].scatter(x="age", y="price",data=df[df["type"]=="truck"],\
            color=colors["truck"],alpha=0.2)
axs[0].set_title('truck type', fontsize=15)
axs[0].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-2,40])
axs[0].set_ylim([-2000,70000])
axs[0].legend(["truck"],loc="upper right", fontsize=10)
axs[1].scatter(x="age", y="price",data=df[df["type"]=="pickup"],\
            color=colors["pickup"],alpha=0.2)
axs[1].set_title('pickup type', fontsize=15)
axs[1].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1].set_xlim([-2,40])
axs[1].set_ylim([-2000,70000])
axs[1].legend(["pickup"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14c99374c7f0>


import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'8 cylinders':'red', '6 cylinders':'orange'}
fig.suptitle("Price and Age of Secondhand Ford f-150 Categorized by Cylinders", fontsize=20)
axs[0].scatter(x="age", y="price",data=df[df["cylinders"]=="8 cylinders"],\
            color=colors["8 cylinders"],alpha=0.2)
axs[0].set_title('8 cylinders', fontsize=15)
axs[0].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-2,40])
axs[0].set_ylim([-2000,70000])
axs[0].legend(["8 cylinders"],loc="upper right", fontsize=10)
axs[1].scatter(x="age", y="price",data=df[df["cylinders"]=="6 cylinders"],\
            color=colors["6 cylinders"],alpha=0.2)
axs[1].set_title('6 cylinders', fontsize=15)
axs[1].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1].set_xlim([-2,40])
axs[1].set_ylim([-2000,70000])
axs[1].legend(["6 cylinders"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14c999b8cbe0>


import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'truck':'red', 'pickup':'orange'}
fig.suptitle("Price and Logged Age of Secondhand Ford f-150 Categorized by Cylinders", fontsize=20)
axs[0].scatter(x=np.log(df[df["cylinders"]=="8 cylinders"]["age"]), y="price",data=df[df["cylinders"]=="8 cylinders"],\
            color=colors["truck"],alpha=0.2)
axs[0].set_title('8 cylinders', fontsize=15)
axs[0].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-0.5,4])
axs[0].set_ylim([-2000,70000])
axs[0].legend(["8 cylinders"],loc="upper right", fontsize=10)
axs[1].scatter(x=np.log(df[df["cylinders"]=="6 cylinders"]["age"]), y="price",data=df[df["cylinders"]=="6 cylinders"],\
            color=colors["pickup"],alpha=0.2)
axs[1].set_title('6 cylinders', fontsize=15)
axs[1].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1].set_xlim([-0.5,4])
axs[1].set_ylim([-2000,70000])
axs[1].legend(["6 cylinders"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14c996a376a0>


import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'8 cylinders':'red', '6 cylinders':'orange'}
fig.suptitle("Price and Mileage of Secondhand Ford f-150 Categorized by Cylinders", fontsize=20)
axs[0].scatter(x="odometer", y="price",data=df[df["cylinders"]=="8 cylinders"],\
            color=colors["8 cylinders"],alpha=0.2)
axs[0].set_title('8 cylinders', fontsize=15)
axs[0].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-2000,320000])
axs[0].set_ylim([-2000,70000])
axs[0].legend(["truck"],loc="upper right", fontsize=10)
axs[1].scatter(x="odometer", y="price",data=df[df["cylinders"]=="6 cylinders"],\
            color=colors["6 cylinders"],alpha=0.2)
axs[1].set_title('6 cylinders', fontsize=15)
axs[1].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1].set_xlim([-2000,320000])
axs[1].set_ylim([-2000,70000])
axs[1].legend(["6 cylinders"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14c996adaec0>


import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'8 cylinders':'red', '6 cylinders':'orange'}
fig.suptitle("Age and Mileage of Secondhand Ford f-150 Categorized by Cylinders", fontsize=20)
axs[0].scatter(x="age", y="odometer",data=df[df["cylinders"]=="8 cylinders"],\
            color=colors["8 cylinders"],alpha=0.2)
axs[0].set_title('8 cylinders', fontsize=15)
axs[0].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-2,40])
axs[0].set_ylim([-2000,320000])
axs[0].legend(["8 cylinders"],loc="upper right", fontsize=10)
axs[1].scatter(x="age", y="odometer",data=df[df["cylinders"]=="6 cylinders"],\
            color=colors["6 cylinders"],alpha=0.2)
axs[1].set_title('6 cylinders', fontsize=15)
axs[1].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[1].set_ylabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1].set_xlim([-2,40])
axs[1].set_ylim([-2000,320000])
axs[1].legend(["6 cylinders"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14c996811b70>


import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'truck':'red', 'pickup':'orange'}
fig.suptitle("Age and Mileage of Secondhand Ford f-150 Categorized by Type", fontsize=20)
axs[0].scatter(x="age", y="odometer",data=df[df["type"]=="truck"],\
            color=colors["truck"],alpha=0.2)
axs[0].set_title('truck type', fontsize=15)
axs[0].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-2,40])
axs[0].set_ylim([-2000,320000])
axs[0].legend(["truck"],loc="upper right", fontsize=10)
axs[1].scatter(x="age", y="odometer",data=df[df["type"]=="pickup"],\
            color=colors["pickup"],alpha=0.2)
axs[1].set_title('pickup type', fontsize=15)
axs[1].set_xlabel("Vehicle Age (Year)", fontsize=10)
axs[1].set_ylabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1].set_xlim([-2,40])
axs[1].set_ylim([-2000,320000])
axs[1].legend(["pickup"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8cbe4a620>


import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'truck':'red', 'pickup':'orange'}
fig.suptitle("Price and Mileage of Secondhand Ford f-150 Categorized by Type", fontsize=20)
axs[0].scatter(x="odometer", y="price",data=df[df["type"]=="truck"],\
            color=colors["truck"],alpha=0.2)
axs[0].set_title('truck type', fontsize=15)
axs[0].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-2000,320000])
axs[0].set_ylim([-2000,70000])
axs[0].legend(["truck"],loc="upper right", fontsize=10)
axs[1].scatter(x="odometer", y="price",data=df[df["type"]=="pickup"],\
            color=colors["pickup"],alpha=0.2)
axs[1].set_title('pickup type', fontsize=15)
axs[1].set_xlabel("Vehicle Mileage (Mile)", fontsize=10)
axs[1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1].set_xlim([-2000,320000])
axs[1].set_ylim([-2000,70000])
axs[1].legend(["pickup"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8cd284d30>


import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'truck':'red', 'pickup':'orange'}
fig.suptitle("Price and Logged Mileage of Secondhand Ford f-150 Categorized by Type", fontsize=20)
axs[0].scatter(x=np.log(df[df["type"]=="truck"]["odometer"]), y="price",data=df[df["type"]=="truck"],\
            color=colors["truck"],alpha=0.2)
axs[0].set_title('truck type', fontsize=15)
axs[0].set_xlabel("Logged Vehicle Mileage (log(Mile))", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([9,13])
axs[0].set_ylim([-2000,70000])
axs[0].legend(["truck"],loc="upper right", fontsize=10)
axs[1].scatter(x=np.log(df[df["type"]=="pickup"]["odometer"]), y="price",data=df[df["type"]=="pickup"],\
            color=colors["pickup"],alpha=0.2)
axs[1].set_title('pickup type', fontsize=15)
axs[1].set_xlabel("Logged Vehicle Mileage (log(Mile))", fontsize=10)
axs[1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1].set_xlim([9,13])
axs[1].set_ylim([-2000,70000])
axs[1].legend(["pickup"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8cb5f1a80>


import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(1, 2, figsize=(20, 10))
colors = {'truck':'red', 'pickup':'orange'}
fig.suptitle("Price and Logged Age of Secondhand Ford f-150 Categorized by Type", fontsize=20)
axs[0].scatter(x=np.log(df[df["type"]=="truck"]["age"]), y="price",data=df[df["type"]=="truck"],\
            color=colors["truck"],alpha=0.2)
axs[0].set_title('truck type', fontsize=15)
axs[0].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[0].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[0].set_xlim([-0.2,4])
axs[0].set_ylim([-2000,70000])
axs[0].legend(["truck"],loc="upper right", fontsize=10)
axs[1].scatter(x=np.log(df[df["type"]=="pickup"]["age"]), y="price",data=df[df["type"]=="pickup"],\
            color=colors["pickup"],alpha=0.2)
axs[1].set_title('pickup type', fontsize=15)
axs[1].set_xlabel("Logged Vehicle Age (log(Year))", fontsize=10)
axs[1].set_ylabel("Vehicle Price (USD)", fontsize=10)
axs[1].set_xlim([-0.2,4])
axs[1].set_ylim([-2000,70000])
axs[1].legend(["pickup"],loc="upper right", fontsize=10)

<matplotlib.legend.Legend at 0x14b8cb21fe20>


import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', \
          'good':'yellow', 'like new':'blue', \
          'new':'green', 'salvage':'purple'}
plt.figure(figsize=(15,10))
plt.scatter(x="age", y="price",data=df, \
            c=df["condition"].astype('category').map(colors),alpha=0.3)
plt.xlabel("Vehicle Age", fontsize=15)
plt.ylabel("Vehicle Price", fontsize=15)
plt.title("Price and Age of Secondhand Ford f-150", fontsize=20)
plt.legend(['excellent: red', 'fair: orange', \
          'good: yellow', 'like new: blue', \
          'new: green', 'salvage: purple'], loc ="upper right", fontsize=15)
plt.xlim([-5,50])
plt.show()


import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple'}
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"],c=df["condition"].astype('category').map(colors),alpha=0.5)
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5,40])
plt.show()


import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple'}
alphas = {'excellent':0.5, 'fair':0.5, 'good':0.5, 'like new':0.5, 'new':0.5, 'salvage':0.5,'none':0}
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"],c=df["condition"].astype('category').map(colors),alpha=df["condition"].astype('category').map(alphas))
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5,40])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"])
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"],alpha=0.3)
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"],alpha=0.1)
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()


import numpy as np
import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple','none':'pink'}
alphas = {'excellent':0.5, 'fair':0.5, 'good':0.5, 'like new':0.5, 'new':0.5, 'salvage':0.5,'none':0}
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"],c=df["condition"].astype('category').map(colors),alpha=df["condition"].astype('category').map(alphas))
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple','none':'pink'}
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"],c=df["condition"].astype('category').map(colors),alpha=0.7)
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["odometer"], df["price"])
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 500000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple','none':'pink'}
plt.scatter(df["odometer"], df["price"],c=df["condition"].astype('category').map(colors),alpha=0.7)
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 500000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["odometer"], df["price"])
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple','none':'pink'}
plt.scatter(df["odometer"], df["price"],c=df["condition"].astype('category').map(colors),alpha=0.7)
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple','none':'pink'}
alphas = {'excellent':0.5, 'fair':0.5, 'good':0.5, 'like new':0.5, 'new':0.5, 'salvage':0.5,'none':0}
plt.scatter(df["odometer"], df["price"],c=df["condition"].astype('category').map(colors),alpha=df["condition"].astype('category').map(alphas))
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["odometer"]), df["price"])
plt.xlabel("Mileage Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([8, 13])
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple','none':'pink'}
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"],c=df["condition"].astype('category').map(colors),alpha=0.7)
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
colors = {'excellent':'red', 'fair':'orange', 'good':'yellow', 'like new':'blue', 'new':'green', 'salvage':'purple','none':'pink'}
alphas = {'excellent':0.5, 'fair':0.5, 'good':0.5, 'like new':0.5, 'new':0.5, 'salvage':0.5,'none':0}
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"],c=df["condition"].astype('category').map(colors),alpha=df["condition"].astype('category').map(alphas))
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


df = vehicle_2[vehicle_2["model"]=='silverado 1500']
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)

/tmp/ipykernel_1816880/2323307577.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1816880/2323307577.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1816880/2323307577.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()


import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-2,25])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"])
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["odometer"], df["price"])
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["odometer"]), df["price"])
plt.xlabel("Mileage Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([8, 13])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


df = vehicle_2[vehicle_2["model"]=='1500']
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)

/tmp/ipykernel_1816880/2848382175.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1816880/2848382175.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1816880/2848382175.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"])
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["odometer"], df["price"])
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["odometer"]), df["price"])
plt.xlabel("Mileage Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([8, 13])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


df = vehicle_2[vehicle_2["model"]=='camry']
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)

/tmp/ipykernel_1816880/1492278436.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1816880/1492278436.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1816880/1492278436.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"])
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["odometer"], df["price"])
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["odometer"]), df["price"])
plt.xlabel("Mileage Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([8, 13])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


df = vehicle_2[vehicle_2["model"]=='silverado']
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)

/tmp/ipykernel_1816880/3604883723.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1816880/3604883723.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1816880/3604883723.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"])
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["odometer"], df["price"])
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["odometer"]), df["price"])
plt.xlabel("Mileage Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([8, 13])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


df = vehicle_2[vehicle_2["model"]=='accord']
df["year"] = df["year"].astype(int)
df["age"] = 2021 - df["year"]
df["price"] = df["price"].astype(float)

/tmp/ipykernel_1816880/1156269704.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1816880/1156269704.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1816880/1156269704.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)


import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["price"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["age"]), df["price"])
plt.xlabel("Age Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.show()

/home/yifeisun/.local/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["odometer"], df["price"])
plt.xlabel("Mileage", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-5000, 300000])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(np.log(df["odometer"]), df["price"])
plt.xlabel("Mileage Logged", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([8, 13])
plt.show()


import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20,15))
plt.scatter(df["age"], df["odometer"])
plt.xlabel("Age", fontsize=15)
plt.ylabel("Mileage", fontsize=15)
plt.xlim([-3,50])
plt.ylim([-10000,350000])
plt.show()


import matplotlib.pyplot as plt
plt.figure(figsize=(15,10))
for i in ['f-150']:
    df = vehicle_2[vehicle_2["model"]==i]
    df["year"] = df["year"].astype(int)
    df["age"] = 2021 - df["year"]
    df["price"] = df["price"].astype(float)
    plt.plot(df["age"], df["price"], vehicle_2["model"])
plt.title("Count Percentage of Movies of Different Genres through the years (First 5 Genres)", fontsize=20)
plt.xlabel("Year", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-1, 30])
plt.legend()
plt.show()

/tmp/ipykernel_1816880/16990256.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1816880/16990256.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1816880/16990256.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [19], in <cell line: 3>()
      6     df["age"] = 2021 - df["year"]
      7     df["price"] = df["price"].astype(float)
----> 8     plt.plot(df["age"], df["price"], vehicle_2["model"])
      9 plt.title("Count Percentage of Movies of Different Genres through the years (First 5 Genres)", fontsize=20)
     10 plt.xlabel("Year", fontsize=15)

File ~/.local/lib/python3.10/site-packages/matplotlib/pyplot.py:2740, in plot(scalex, scaley, data, *args, **kwargs)
   2738 @_copy_docstring_and_deprecators(Axes.plot)
   2739 def plot(*args, scalex=True, scaley=True, data=None, **kwargs):
-> 2740     return gca().plot(
   2741         *args, scalex=scalex, scaley=scaley,
   2742         **({"data": data} if data is not None else {}), **kwargs)

File ~/.local/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1662, in Axes.plot(self, scalex, scaley, data, *args, **kwargs)
   1419 """
   1420 Plot y versus x as lines and/or markers.
   1421 
   (...)
   1659 (``'green'``) or hex strings (``'#008000'``).
   1660 """
   1661 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1662 lines = [*self._get_lines(*args, data=data, **kwargs)]
   1663 for line in lines:
   1664     self.add_line(line)

File ~/.local/lib/python3.10/site-packages/matplotlib/axes/_base.py:311, in _process_plot_var_args.__call__(self, data, *args, **kwargs)
    309     this += args[0],
    310     args = args[1:]
--> 311 yield from self._plot_args(
    312     this, kwargs, ambiguous_fmt_datakey=ambiguous_fmt_datakey)

File ~/.local/lib/python3.10/site-packages/matplotlib/axes/_base.py:501, in _process_plot_var_args._plot_args(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)
    499     self.axes.xaxis.update_units(x)
    500 if self.axes.yaxis is not None:
--> 501     self.axes.yaxis.update_units(y)
    503 if x.shape[0] != y.shape[0]:
    504     raise ValueError(f"x and y must have same first dimension, but "
    505                      f"have shapes {x.shape} and {y.shape}")

File ~/.local/lib/python3.10/site-packages/matplotlib/axis.py:1608, in Axis.update_units(self, data)
   1606 neednew = self.converter != converter
   1607 self.converter = converter
-> 1608 default = self.converter.default_units(data, self)
   1609 if default is not None and self.units is None:
   1610     self.set_units(default)

File ~/.local/lib/python3.10/site-packages/matplotlib/category.py:116, in StrCategoryConverter.default_units(data, axis)
    114 # the conversion call stack is default_units -> axis_info -> convert
    115 if axis.units is None:
--> 116     axis.set_units(UnitData(data))
    117 else:
    118     axis.units.update(data)

File ~/.local/lib/python3.10/site-packages/matplotlib/category.py:192, in UnitData.__init__(self, data)
    190 self._counter = itertools.count()
    191 if data is not None:
--> 192     self.update(data)

File ~/.local/lib/python3.10/site-packages/matplotlib/category.py:227, in UnitData.update(self, data)
    224 convertible = True
    225 for val in OrderedDict.fromkeys(data):
    226     # OrderedDict just iterates over unique values in data.
--> 227     _api.check_isinstance((str, bytes), value=val)
    228     if convertible:
    229         # this will only be called so long as convertible is True.
    230         convertible = self._str_is_convertible(val)

File ~/.local/lib/python3.10/site-packages/matplotlib/_api/__init__.py:93, in check_isinstance(_types, **kwargs)
     91     names.remove("None")
     92     names.append("None")
---> 93 raise TypeError(
     94     "{!r} must be an instance of {}, not a {}".format(
     95         k,
     96         ", ".join(names[:-1]) + " or " + names[-1]
     97         if len(names) > 1 else names[0],
     98         type_name(type(v))))

TypeError: 'value' must be an instance of str or bytes, not a float


import matplotlib.pyplot as plt
plt.figure(figsize=(15,10))
for i in list(set(vehicle_2["model"]))[:1]:
    df = vehicle_2[vehicle_2["model"]==i]
    df["year"] = df["year"].astype(int)
    df["age"] = 2021 - df["year"]
    df["price"] = df["price"].astype(float)
    plt.plot(df["age"], df["price"], vehicle_2["model"])
plt.title("Count Percentage of Movies of Different Genres through the years (First 5 Genres)", fontsize=20)
plt.xlabel("Year", fontsize=15)
plt.ylabel("Price", fontsize=15)
plt.xlim([-1, 30])
plt.legend()
plt.show()

/tmp/ipykernel_1816880/4170661589.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["year"] = df["year"].astype(int)
/tmp/ipykernel_1816880/4170661589.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["age"] = 2021 - df["year"]
/tmp/ipykernel_1816880/4170661589.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df["price"] = df["price"].astype(float)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [9], in <cell line: 3>()
      6     df["age"] = 2021 - df["year"]
      7     df["price"] = df["price"].astype(float)
----> 8     plt.plot(df["age"], df["price"], vehicle_2["model"])
      9 plt.title("Count Percentage of Movies of Different Genres through the years (First 5 Genres)", fontsize=20)
     10 plt.xlabel("Year", fontsize=15)

File ~/.local/lib/python3.10/site-packages/matplotlib/pyplot.py:2740, in plot(scalex, scaley, data, *args, **kwargs)
   2738 @_copy_docstring_and_deprecators(Axes.plot)
   2739 def plot(*args, scalex=True, scaley=True, data=None, **kwargs):
-> 2740     return gca().plot(
   2741         *args, scalex=scalex, scaley=scaley,
   2742         **({"data": data} if data is not None else {}), **kwargs)

File ~/.local/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1662, in Axes.plot(self, scalex, scaley, data, *args, **kwargs)
   1419 """
   1420 Plot y versus x as lines and/or markers.
   1421 
   (...)
   1659 (``'green'``) or hex strings (``'#008000'``).
   1660 """
   1661 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1662 lines = [*self._get_lines(*args, data=data, **kwargs)]
   1663 for line in lines:
   1664     self.add_line(line)

File ~/.local/lib/python3.10/site-packages/matplotlib/axes/_base.py:311, in _process_plot_var_args.__call__(self, data, *args, **kwargs)
    309     this += args[0],
    310     args = args[1:]
--> 311 yield from self._plot_args(
    312     this, kwargs, ambiguous_fmt_datakey=ambiguous_fmt_datakey)

File ~/.local/lib/python3.10/site-packages/matplotlib/axes/_base.py:501, in _process_plot_var_args._plot_args(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)
    499     self.axes.xaxis.update_units(x)
    500 if self.axes.yaxis is not None:
--> 501     self.axes.yaxis.update_units(y)
    503 if x.shape[0] != y.shape[0]:
    504     raise ValueError(f"x and y must have same first dimension, but "
    505                      f"have shapes {x.shape} and {y.shape}")

File ~/.local/lib/python3.10/site-packages/matplotlib/axis.py:1608, in Axis.update_units(self, data)
   1606 neednew = self.converter != converter
   1607 self.converter = converter
-> 1608 default = self.converter.default_units(data, self)
   1609 if default is not None and self.units is None:
   1610     self.set_units(default)

File ~/.local/lib/python3.10/site-packages/matplotlib/category.py:116, in StrCategoryConverter.default_units(data, axis)
    114 # the conversion call stack is default_units -> axis_info -> convert
    115 if axis.units is None:
--> 116     axis.set_units(UnitData(data))
    117 else:
    118     axis.units.update(data)

File ~/.local/lib/python3.10/site-packages/matplotlib/category.py:192, in UnitData.__init__(self, data)
    190 self._counter = itertools.count()
    191 if data is not None:
--> 192     self.update(data)

File ~/.local/lib/python3.10/site-packages/matplotlib/category.py:227, in UnitData.update(self, data)
    224 convertible = True
    225 for val in OrderedDict.fromkeys(data):
    226     # OrderedDict just iterates over unique values in data.
--> 227     _api.check_isinstance((str, bytes), value=val)
    228     if convertible:
    229         # this will only be called so long as convertible is True.
    230         convertible = self._str_is_convertible(val)

File ~/.local/lib/python3.10/site-packages/matplotlib/_api/__init__.py:93, in check_isinstance(_types, **kwargs)
     91     names.remove("None")
     92     names.append("None")
---> 93 raise TypeError(
     94     "{!r} must be an instance of {}, not a {}".format(
     95         k,
     96         ", ".join(names[:-1]) + " or " + names[-1]
     97         if len(names) > 1 else names[0],
     98         type_name(type(v))))

TypeError: 'value' must be an instance of str or bytes, not a float


pd.to_datetime(vehicle_2["posting_date"])

27        2021-05-04 12:31:18-05:00
28        2021-05-04 12:31:08-05:00
29        2021-05-04 12:31:25-05:00
30        2021-05-04 10:41:31-05:00
31        2021-05-03 14:02:03-05:00
                    ...            
426875    2021-04-04 03:21:31-06:00
426876    2021-04-04 03:21:29-06:00
426877    2021-04-04 03:21:17-06:00
426878    2021-04-04 03:21:11-06:00
426879    2021-04-04 03:21:07-06:00
Name: posting_date, Length: 409234, dtype: object


pd.to_datetime(vehicle_2["posting_date"]).sort_values()

271444    2021-04-04 03:00:25-04:00
271443    2021-04-04 03:00:51-04:00
271442    2021-04-04 03:01:16-04:00
419122    2021-04-04 02:03:15-05:00
263503    2021-04-04 01:10:12-06:00
                    ...            
406982    2021-05-04 20:58:48-07:00
414522    2021-05-04 23:59:45-04:00
422031    2021-05-04 23:07:07-05:00
418366    2021-05-04 23:12:08-05:00
422030    2021-05-04 23:24:09-05:00
Name: posting_date, Length: 409234, dtype: object


set(pd.to_datetime(vehicle_2["posting_date"]).year)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [23], in <cell line: 1>()
----> 1 set(pd.to_datetime(vehicle_2["posting_date"]).year)

File ~/.local/lib/python3.10/site-packages/pandas/core/generic.py:5902, in NDFrame.__getattr__(self, name)
   5895 if (
   5896     name not in self._internal_names_set
   5897     and name not in self._metadata
   5898     and name not in self._accessors
   5899     and self._info_axis._can_hold_identifiers_and_holds_name(name)
   5900 ):
   5901     return self[name]
-> 5902 return object.__getattribute__(self, name)

AttributeError: 'Series' object has no attribute 'year'


set(vehicle["region"].tolist())

{'SF bay area',
 'abilene',
 'akron / canton',
 'albany',
 'albuquerque',
 'altoona-johnstown',
 'amarillo',
 'ames',
 'anchorage / mat-su',
 'ann arbor',
 'annapolis',
 'appleton-oshkosh-FDL',
 'asheville',
 'ashtabula',
 'athens',
 'atlanta',
 'auburn',
 'augusta',
 'austin',
 'bakersfield',
 'baltimore',
 'baton rouge',
 'battle creek',
 'beaumont / port arthur',
 'bellingham',
 'bemidji',
 'bend',
 'billings',
 'binghamton',
 'birmingham',
 'bismarck',
 'bloomington',
 'bloomington-normal',
 'boise',
 'boone',
 'boston',
 'boulder',
 'bowling green',
 'bozeman',
 'brainerd',
 'brownsville',
 'brunswick',
 'buffalo',
 'butte',
 'cape cod / islands',
 'catskills',
 'cedar rapids',
 'central NJ',
 'central louisiana',
 'central michigan',
 'champaign urbana',
 'charleston',
 'charlotte',
 'charlottesville',
 'chattanooga',
 'chautauqua',
 'chicago',
 'chico',
 'chillicothe',
 'cincinnati',
 'clarksville',
 'cleveland',
 'clovis / portales',
 'college station',
 'colorado springs',
 'columbia',
 'columbia / jeff city',
 'columbus',
 'cookeville',
 'corpus christi',
 'corvallis/albany',
 'cumberland valley',
 'dallas / fort worth',
 'danville',
 'dayton / springfield',
 'daytona beach',
 'decatur',
 'deep east texas',
 'del rio / eagle pass',
 'delaware',
 'denver',
 'des moines',
 'detroit metro',
 'dothan',
 'dubuque',
 'duluth / superior',
 'east idaho',
 'east oregon',
 'eastern CO',
 'eastern CT',
 'eastern NC',
 'eastern kentucky',
 'eastern montana',
 'eastern panhandle',
 'eastern shore',
 'eau claire',
 'el paso',
 'elko',
 'elmira-corning',
 'erie',
 'eugene',
 'evansville',
 'fairbanks',
 'fargo / moorhead',
 'farmington',
 'fayetteville',
 'finger lakes',
 'flagstaff / sedona',
 'flint',
 'florence',
 'florence / muscle shoals',
 'florida keys',
 'fort collins / north CO',
 'fort dodge',
 'fort smith',
 'fort smith, AR',
 'fort wayne',
 'frederick',
 'fredericksburg',
 'fresno / madera',
 'ft myers / SW florida',
 'gadsden-anniston',
 'gainesville',
 'galveston',
 'glens falls',
 'gold country',
 'grand forks',
 'grand island',
 'grand rapids',
 'great falls',
 'green bay',
 'greensboro',
 'greenville / upstate',
 'gulfport / biloxi',
 'hanford-corcoran',
 'harrisburg',
 'harrisonburg',
 'hartford',
 'hattiesburg',
 'hawaii',
 'heartland florida',
 'helena',
 'hickory / lenoir',
 'high rockies',
 'hilton head',
 'holland',
 'houma',
 'houston',
 'hudson valley',
 'humboldt county',
 'huntington-ashland',
 'huntsville / decatur',
 'imperial county',
 'indianapolis',
 'inland empire',
 'iowa city',
 'ithaca',
 'jackson',
 'jacksonville',
 'janesville',
 'jersey shore',
 'jonesboro',
 'joplin',
 'kalamazoo',
 'kalispell',
 'kansas city',
 'kansas city, MO',
 'kenai peninsula',
 'kennewick-pasco-richland',
 'kenosha-racine',
 'killeen / temple / ft hood',
 'kirksville',
 'klamath falls',
 'knoxville',
 'kokomo',
 'la crosse',
 'la salle co',
 'lafayette',
 'lafayette / west lafayette',
 'lake charles',
 'lake of the ozarks',
 'lakeland',
 'lancaster',
 'lansing',
 'laredo',
 'las cruces',
 'las vegas',
 'lawrence',
 'lawton',
 'lehigh valley',
 'lewiston / clarkston',
 'lexington',
 'lima / findlay',
 'lincoln',
 'little rock',
 'logan',
 'long island',
 'los angeles',
 'louisville',
 'lubbock',
 'lynchburg',
 'macon / warner robins',
 'madison',
 'maine',
 'manhattan',
 'mankato',
 'mansfield',
 'mason city',
 'mattoon-charleston',
 'mcallen / edinburg',
 'meadville',
 'medford-ashland',
 'memphis',
 'mendocino county',
 'merced',
 'meridian',
 'milwaukee',
 'minneapolis / st paul',
 'missoula',
 'mobile',
 'modesto',
 'mohave county',
 'monroe',
 'monterey bay',
 'montgomery',
 'morgantown',
 'moses lake',
 'muncie / anderson',
 'muskegon',
 'myrtle beach',
 'nashville',
 'new hampshire',
 'new haven',
 'new orleans',
 'new river valley',
 'new york city',
 'norfolk / hampton roads',
 'north central FL',
 'north dakota',
 'north jersey',
 'north mississippi',
 'north platte',
 'northeast SD',
 'northern WI',
 'northern michigan',
 'northern panhandle',
 'northwest CT',
 'northwest GA',
 'northwest KS',
 'northwest OK',
 'ocala',
 'odessa / midland',
 'ogden-clearfield',
 'okaloosa / walton',
 'oklahoma city',
 'olympic peninsula',
 'omaha / council bluffs',
 'oneonta',
 'orange county',
 'oregon coast',
 'orlando',
 'outer banks',
 'owensboro',
 'palm springs',
 'panama city',
 'parkersburg-marietta',
 'pensacola',
 'peoria',
 'philadelphia',
 'phoenix',
 'pierre / central SD',
 'pittsburgh',
 'plattsburgh-adirondacks',
 'poconos',
 'port huron',
 'portland',
 'potsdam-canton-massena',
 'prescott',
 'provo / orem',
 'pueblo',
 'pullman / moscow',
 'quad cities, IA/IL',
 'raleigh / durham / CH',
 'rapid city / west SD',
 'reading',
 'redding',
 'reno / tahoe',
 'rhode island',
 'richmond',
 'roanoke',
 'rochester',
 'rockford',
 'roseburg',
 'roswell / carlsbad',
 'sacramento',
 'saginaw-midland-baycity',
 'salem',
 'salina',
 'salt lake city',
 'san angelo',
 'san antonio',
 'san diego',
 'san luis obispo',
 'san marcos',
 'sandusky',
 'santa barbara',
 'santa fe / taos',
 'santa maria',
 'sarasota-bradenton',
 'savannah / hinesville',
 'scottsbluff / panhandle',
 'scranton / wilkes-barre',
 'seattle-tacoma',
 'sheboygan',
 'show low',
 'shreveport',
 'sierra vista',
 'sioux city',
 'sioux falls / SE SD',
 'siskiyou county',
 'skagit / island / SJI',
 'south bend / michiana',
 'south coast',
 'south dakota',
 'south florida',
 'south jersey',
 'southeast IA',
 'southeast KS',
 'southeast alaska',
 'southeast missouri',
 'southern WV',
 'southern illinois',
 'southern maryland',
 'southwest KS',
 'southwest MN',
 'southwest MS',
 'southwest TX',
 'southwest VA',
 'southwest michigan',
 'space coast',
 "spokane / coeur d'alene",
 'springfield',
 'st augustine',
 'st cloud',
 'st george',
 'st joseph',
 'st louis',
 'st louis, MO',
 'state college',
 'statesboro',
 'stillwater',
 'stockton',
 'susanville',
 'syracuse',
 'tallahassee',
 'tampa bay area',
 'terre haute',
 'texarkana',
 'texoma',
 'the thumb',
 'toledo',
 'topeka',
 'treasure coast',
 'tri-cities',
 'tucson',
 'tulsa',
 'tuscaloosa',
 'tuscarawas co',
 'twin falls',
 'twin tiers NY/PA',
 'tyler / east TX',
 'upper peninsula',
 'utica-rome-oneida',
 'valdosta',
 'ventura county',
 'vermont',
 'victoria',
 'visalia-tulare',
 'waco',
 'washington, DC',
 'waterloo / cedar falls',
 'watertown',
 'wausau',
 'wenatchee',
 'west virginia (old)',
 'western IL',
 'western KY',
 'western maryland',
 'western massachusetts',
 'western slope',
 'wichita',
 'wichita falls',
 'williamsport',
 'wilmington',
 'winchester',
 'winston-salem',
 'worcester / central MA',
 'wyoming',
 'yakima',
 'york',
 'youngstown',
 'yuba-sutter',
 'yuma',
 'zanesville / cambridge'}


len(set(vehicle["region"].tolist()))

404


vehicle_2["url"].iloc[4567]

'https://tuscaloosa.craigslist.org/cto/d/tuscaloosa-1968-chevrolet-caprice/7312092765.html'


carprice = pd.read_csv("CarPrice_Assignment.csv")
carprice

	Hourly_Transaction	hour	dehourfied_hourly_Transaction
2021-04-04 00:00:00	1	0	-70.419355
2021-04-04 01:00:00	10	1	-39.483871
2021-04-04 02:00:00	5	2	-30.741935
2021-04-04 03:00:00	63	3	-9.580645
2021-04-04 04:00:00	69	4	-35.129032
2021-04-04 05:00:00	89	5	-33.258065
2021-04-04 06:00:00	91	6	-137.580645
2021-04-04 07:00:00	133	7	-340.483871
2021-04-04 08:00:00	282	8	-555.419355
2021-04-04 09:00:00	365	9	-832.870968
2021-04-04 10:00:00	561	10	-801.580645
2021-04-04 11:00:00	403	11	-964.677419
2021-04-04 12:00:00	355	12	-853.741935
2021-04-04 13:00:00	324	13	-831.451613
2021-04-04 14:00:00	272	14	-759.548387
2021-04-04 15:00:00	211	15	-730.193548
2021-04-04 16:00:00	115	16	-765.806452
2021-04-04 17:00:00	85	17	-648.870968
2021-04-04 18:00:00	86	18	-463.903226
2021-04-04 19:00:00	99	19	-317.774194
2021-04-04 20:00:00	74	20	-261.741935
2021-04-04 21:00:00	131	21	-154.322581
2021-04-04 22:00:00	58	22	-133.032258
2021-04-04 23:00:00	29	23	-85.516129
2021-04-05 00:00:00	32	0	-39.419355
2021-04-05 01:00:00	14	1	-35.483871
2021-04-05 02:00:00	16	2	-19.741935
2021-04-05 03:00:00	76	3	3.419355
2021-04-05 04:00:00	101	4	-3.129032
2021-04-05 05:00:00	71	5	-51.258065
2021-04-05 06:00:00	107	6	-121.580645
2021-04-05 07:00:00	219	7	-254.483871
2021-04-05 08:00:00	433	8	-404.419355
2021-04-05 09:00:00	634	9	-563.870968
2021-04-05 10:00:00	950	10	-412.580645
2021-04-05 11:00:00	856	11	-511.677419
2021-04-05 12:00:00	738	12	-470.741935
2021-04-05 13:00:00	665	13	-490.451613
2021-04-05 14:00:00	680	14	-351.548387
2021-04-05 15:00:00	507	15	-434.193548
2021-04-05 16:00:00	374	16	-506.806452
2021-04-05 17:00:00	289	17	-444.870968
2021-04-05 18:00:00	181	18	-368.903226
2021-04-05 19:00:00	179	19	-237.774194
2021-04-05 20:00:00	162	20	-173.741935
2021-04-05 21:00:00	163	21	-122.322581
2021-04-05 22:00:00	124	22	-67.032258
2021-04-05 23:00:00	51	23	-63.516129
2021-04-06 00:00:00	28	0	-43.419355
2021-04-06 01:00:00	14	1	-35.483871

	daily_transaction	weekday	deweekfied_daily_transaction
2021-04-04	3911	Sunday	-4389.20
2021-04-05	7622	Monday	-8198.60
2021-04-06	7041	Tuesday	-7959.20
2021-04-07	7780	Wednesday	-4825.25
2021-04-08	8897	Thursday	-4998.00
2021-04-09	9288	Friday	-7638.50
2021-04-10	7350	Saturday	-7025.00
2021-04-11	5127	Sunday	-3173.20
2021-04-12	8803	Monday	-7017.60
2021-04-13	8464	Tuesday	-6536.20
2021-04-14	10214	Wednesday	-2391.25
2021-04-15	10417	Thursday	-3478.00
2021-04-16	11404	Friday	-5522.50
2021-04-17	10166	Saturday	-4209.00
2021-04-18	6257	Sunday	-2043.20
2021-04-19	10910	Monday	-4910.60
2021-04-20	11157	Tuesday	-3843.20
2021-04-21	12943	Wednesday	337.75
2021-04-22	14920	Thursday	1025.00
2021-04-23	18898	Friday	1971.50
2021-04-24	13973	Saturday	-402.00
2021-04-25	8430	Sunday	129.80
2021-04-26	16064	Monday	243.40
2021-04-27	17987	Tuesday	2986.80
2021-04-28	19484	Wednesday	6878.75
2021-04-29	21346	Thursday	7451.00
2021-04-30	28116	Friday	11189.50
2021-05-01	26011	Saturday	11636.00
2021-05-02	17776	Sunday	9475.80
2021-05-03	35704	Monday	19883.40
2021-05-04	30352	Tuesday	15351.80

	model and manufacturer	age_depre_rate	mileage_depre_rate (10*6)	age_const	mileage_const	model	manufacturer	age_0_price	mileage_0_price
0	ford f-150	-0.1308	-7.364	10.9655	10.7615	f-150	ford	57843.710160	47169.369045
1	ford escape	-0.1191	-8.536	10.1533	10.1316	escape	ford	25675.692341	25124.531540
2	ford explorer	-0.1531	-10.454	10.7180	10.7270	explorer	ford	45161.489480	45569.777425
3	ford fusion	-0.1274	-8.031	10.0453	9.9310	fusion	ford	23047.210003	20557.888194
4	ford mustang	-0.0910	-8.584	10.5313	10.4475	mustang	ford	37470.153935	34458.121634
...	...	...	...	...	...	...	...	...	...
305	mazda mazda5	-0.0975	-9.329	9.8407	9.8351	mazda5	mazda	18782.859421	18677.969374
306	mazda mazda2	-0.1571	-4.706	10.0953	9.2187	mazda2	mazda	24228.865728	10083.946673
307	mazda cx-5 grand touring	0.2177	19.387	7.8597	7.8765	cx-5 grand touring	mazda	2590.743036	2634.635181
308	mazda cx-5 touring	-0.0620	-2.453	9.8803	9.6496	cx-5 touring	mazda	19541.584258	15515.580631
309	mazda 2	0.0149	-1.589	8.4863	8.8040	2	mazda	4847.895635	6660.834127

	model and manufacturer	age_depre_rate	mileage_depre_rate (10*6)	age_const	mileage_const	model	manufacturer	age_0_price	mileage_0_price	types	types_str
0	ford f-150	-0.1308	-7.364	10.9655	10.7615	f-150	ford	57843.710160	47169.369045	[sedan]	sedan
1	ford escape	-0.1191	-8.536	10.1533	10.1316	escape	ford	25675.692341	25124.531540	[sedan]	sedan
2	ford explorer	-0.1531	-10.454	10.7180	10.7270	explorer	ford	45161.489480	45569.777425	[coupe]	coupe
3	ford fusion	-0.1274	-8.031	10.0453	9.9310	fusion	ford	23047.210003	20557.888194	[sedan]	sedan
4	ford mustang	-0.0910	-8.584	10.5313	10.4475	mustang	ford	37470.153935	34458.121634	[hatchback, sedan]	hatchback, sedan
...	...	...	...	...	...	...	...	...	...	...	...
305	mazda mazda5	-0.0975	-9.329	9.8407	9.8351	mazda5	mazda	18782.859421	18677.969374	[sedan]	sedan
306	mazda mazda2	-0.1571	-4.706	10.0953	9.2187	mazda2	mazda	24228.865728	10083.946673	[hatchback]	hatchback
307	mazda cx-5 grand touring	0.2177	19.387	7.8597	7.8765	cx-5 grand touring	mazda	2590.743036	2634.635181	[mini-van, van]	mini-van, van
308	mazda cx-5 touring	-0.0620	-2.453	9.8803	9.6496	cx-5 touring	mazda	19541.584258	15515.580631	[SUV]	SUV
309	mazda 2	0.0149	-1.589	8.4863	8.8040	2	mazda	4847.895635	6660.834127	[SUV]	SUV

	car_ID	symboling	CarName	fueltype	aspiration	doornumber	carbody	drivewheel	enginelocation	wheelbase	...	enginesize	fuelsystem	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
0	1	3	alfa-romero giulia	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	13495.0
1	2	3	alfa-romero stelvio	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	16500.0
2	3	1	alfa-romero Quadrifoglio	gas	std	two	hatchback	rwd	front	94.5	...	152	mpfi	2.68	3.47	9.0	154	5000	19	26	16500.0
3	4	2	audi 100 ls	gas	std	four	sedan	fwd	front	99.8	...	109	mpfi	3.19	3.40	10.0	102	5500	24	30	13950.0
4	5	2	audi 100ls	gas	std	four	sedan	4wd	front	99.4	...	136	mpfi	3.19	3.40	8.0	115	5500	18	22	17450.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
200	201	-1	volvo 145e (sw)	gas	std	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	9.5	114	5400	23	28	16845.0
201	202	-1	volvo 144ea	gas	turbo	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	8.7	160	5300	19	25	19045.0
202	203	-1	volvo 244dl	gas	std	four	sedan	rwd	front	109.1	...	173	mpfi	3.58	2.87	8.8	134	5500	18	23	21485.0
203	204	-1	volvo 246	diesel	turbo	four	sedan	rwd	front	109.1	...	145	idi	3.01	3.40	23.0	106	4800	26	27	22470.0
204	205	-1	volvo 264gl	gas	turbo	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	9.5	114	5400	19	25	22625.0

type	SUV	convertible	coupe	hatchback	other	pickup	sedan	truck	van	wagon
model
edge	400.0	0.0	0.0	0.0	7.0	0.0	27.0	0.0	0.0	4.0
escape	917.0	0.0	0.0	0.0	17.0	0.0	5.0	1.0	0.0	25.0
expedition	281.0	0.0	0.0	0.0	17.0	0.0	1.0	1.0	0.0	14.0
explorer	736.0	0.0	0.0	0.0	19.0	2.0	0.0	2.0	0.0	14.0
f-150	2.0	0.0	0.0	1.0	40.0	1420.0	0.0	1246.0	1.0	0.0
f-250	0.0	0.0	1.0	0.0	8.0	369.0	0.0	55.0	0.0	0.0
f150	0.0	0.0	0.0	0.0	26.0	120.0	0.0	84.0	0.0	0.0
focus	0.0	0.0	8.0	191.0	37.0	0.0	389.0	0.0	0.0	2.0
fusion	3.0	0.0	0.0	1.0	26.0	0.0	631.0	0.0	0.0	0.0
mustang	7.0	120.0	476.0	0.0	97.0	0.0	2.0	1.0	0.0	0.0